Privacy-Preserving Data Mining

  1. Yamamoto F., Wang L., Ozawa S. " New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis," In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds), Neural Information Processing. ICONIP 2020. pp. 558-569, Lecture Notes in Computer Science, vol 12533. Springer, Cham, December 2020.
  2. Takehiro Tezuka, Lihua Wang, Takuya Hayashi, and Seiichi Ozawa, " A Fast Privacy-Preserving Multi-Layer Perceptron Using Ring-LWE-Based Homomorphic Encryption, " Proc. of 2019 Int. Conf. on Data Mining Workshops (ICDMW), Beijing, China, pp. 37-44, November 2019. doi: 10.1109/ICDMW.2019.00014
  3. Sangwook Kim, Masahiro Omori, Takuya Hayashi, Toshiaki Omor, iLihua Wang, Seiichi Ozawa, "Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption", In: Cheng L., Leung A., Ozawa S. (eds) Neural Information Processing. ICONIP 2018. LNCS, vol 11304. Springer, Cham, pp. 349-358, December 2018
  4. Sangwook Kim, Toshiaki Omori, Masahiro Omori, Takuya Hayashi, Lihua Wang, Seiichi Ozawa, "Privacy-Preserving Naive Bayes Classifier based on Homomorphic Encryption," The 13th International Workshop on Security (IWSEC2018), Sakura Hall, Tohoku University (仙台市), 2018年9月3日
  5. Shohei Kuri, Takuya Hayashi, Toshiaki Omori, Seiichi Ozawa, Yoshinori Aono, Le Trieu Phong, Lihua Wang, Shiho Moriai, "Privacy Preserving Extreme Learning Machine Using Additively Homomorphic Encryption," Proc. of The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), pp. 1350-1357, November 27 - December 1, 2017.

Cybersecrurity

  1. Ishikawa S., Ozawa S., Ban T. " Port-Piece Embedding for Darknet Traffic Features and Clustering of Scan Attacks," In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds), Neural Information Processing. ICONIP 2020. pp. 593-603, Lecture Notes in Computer Science, vol 12533. Springer, Cham, December 2020.
  2. Samuel Ndichu, Sangwook Kim, Seiichi Ozawa, " Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement," CAAI Transactions on Intelligence Technology, vol. 5, no. 10, pp. 184-192, September 2020.
  3. Muhammad Fakhrur Rozi, Sangwook Kim and Seiichi Ozawa, " Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences,” 2020 International Joint Conference on Neural Networks (IJCNN 2020), Online, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207134, July 2020.
  4. Seiichi Ozawa, Tao Ban, Naoki Hashimoto, Junji Nakazato, Jumpei Shimamura, " A Study of IoT Malware Activities Using Association Rule Learning for Darknet Sensor Data," International Journal of Information Security, vol. 19, no. 1, pp. 83-92, January 2020.
  5. Samuel Ndichu, Sangwook Kim, Seiichi Ozawa, Takeshi Misu, Kazuo Makishima, " A Machine Learning Approach to Detection of JavaScript-based Attacks Using AST Features and Paragraph Vectors," Applied Soft Computing, vol. 84, pp. 1-11, November 2019.
  6. Samuel Ndichu Wangar, Seiichi Ozawa, Takeshi Misu, Kouichirou Okada, "Detection of Malicious JavaScript Contents Using Doc2vec Feature Learning", Proc. of 2018 International Joint Conference on Neural Networks (IJCNN 2018), 7 pages, July 2018.
  7. Naoki Hashimoto, Seiichi Ozawa, Tao Ban, Junji Nakazato, Jumpei Shimamura, "A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning," in S. Ozawa, et. al (Eds.) INNS Conference on Big Data and Deep Learning 2018, Procedia Computer Science, Springer, Vol. 144, pp. 118-123, November 2018.
  8. Igor Skrjanc, Seiichi Ozawa, Dejan Dovzan, "Large-Scale Cyber Attacks Monitoring using Evolving Cauchy Possibilistic Clustering." Applied Soft Computing, Vol. 62, pp. 592-601, 2018.
  9. Igor Skrjanc, Seiichi Ozawa, Dejan Dovzan, Ban Tao, Junji Nakazato and Jumpei Shimamura, "Evolving Cauchy Possibilistic Clustering and Its Application to Large-Scale Cyberattack Monitoring," Proc. of The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), pp. 2833-2839, November 27 - December 1, 2017.
  10. Yuki Kawaguchi, Akira Yamada, and Seiichi Ozawa, "AI Web-Contents Analyzer for Monitoring Underground Marketplace," Neural Information Processing: 24th International Conference, ICONIP 2017, Part V, LNCS vol. 10638, pp 888-896，November 2017.
  11. Siti Hajar Aminah Ali, Kiminori Fukase, Seiichi Ozawa, “A Fast Online Learning Algorithm of Radial Basis Function Network with Locality Sensitive Hashing,” Evolving Systems, vol. 7, Issue 3, pp 173-186, September 2016.
  12. Siti Hajar Aminah Ali, Seiichi Ozawa, Tao Ban, Junji Nakazato and Jumpei Shimamura, “A Neural Network Model for Detecting DDoS Attacks Using Darknet Traffic Features,” Proc. of 2016 International Joint Conference on Neural Networks, pp. 2979-2985，July 2016.
  13. Siti Hajar Aminah Ali, Seiichi Ozawa, Tao Ban, Junji Nakazato, and Jumpei Shimamura, "An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing," Journal of Intelligent Learning Systems and Application, Vol.7, No.2, pp. 42-57, May 2015.
  14. Hironori Nishikaze, Seiichi Ozawa, Jun Kitazono, Tao Ban, Junji Nakazato, Jumpei Shimamura, "Large-Scale Monitoring for Cyber Attacks by Using Cluster Information on Darknet Traffic Features,"Proc. of INNS Conf. on Big Data 2015 (INNS-BigData 2015) (in press)
  15. Ali Siti Hajar Aminah, Seiichi Ozawa, Tao Ban, Junji Nakazato, and Jumpei Shimamura, "An Autonomous Online Malicious Spam Mail Detection System Using Extended RBF Network," Proc.of Int. Joint Conf. on Neural Networks 2015 (IJCNN2015-Killarney, Ireland) (in press)
  16. Yuli Dai, Shunsuke Tada, Tao Ban, Junji Nakazato, Jumpei Shimamura, Seiichi Ozawa, "Detecting Malicious Spam Mails: An Online Machine Learning Approach," Neural Information Processing. LNCS 8836, pp 365-372, November 2014.
  17. Nobuaki Furutani, Tao Ban, Junji Nakazato, Jumpei Shimamura, Jun Kitazono, Seiichi Ozawa,"Detection of DDoS Backscatter Based on Traffic Features of Darknet TCP Packets," Proc. Ninth Asia Joint Conference on Information Security (ASIA JCIS), pp. 3-5 September 2014.

SNS Security and Sentiment Analysis

  1. Rodríguez, Pau and Velazquez, Diego and Cucurull, Guillem and Gonfaus, Josep M. and Roca, F. Xavier and Ozawa, Seiichi and Gonzàlez, Jordi, " Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images," Applied Sciences, vol. 10, no. 22, 8170, November 2020.
  2. Seiichi Ozawa, Shun Yoshida, Jun Kitazono, Takahiro Sugawara and Tatsuya Haga, “A Sentiment Polarity Prediction Model Using Transfer Learning and Its Application to SNS Flaming Event Detection,” Proc. of 2016 IEEE Symposium Series on Computational Intelligence, pp. 1-7，Decmber 2016.
  3. Narutaka Awaya, Jun Kitazono, Toshiaki Omori, Seiichi Ozawa, “Stochastic Collapsed Variational Bayesian Inference for Biterm Topic Model,” Proc. of 2016 International Joint Conference on Neural Networks, pp. 3364-3370，July 2016.
  4. Shun Yoshida, Jun Kitazono, Seiichi Ozawa, Takahiro Sugawara, Tatsuya Haga, and Shogo Nakamura, "Sentiment Analysis for Various SNS Media Using Naive Bayes Classifier and Its Application to Flaming Detection," Proc. of 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD), pp. 1-6, December 2014.


Smart Agriculture

  1. Muhammad Taufiq Pratama, Sangwook Kim, Seiichi Ozawa, Takenao Ohkawa, Yuya Chonan, Hiroyuki Tsuji and Noriyuki Murakami, " Deep Learning-based Object Detection for Crop Monitoring in Soybean Fields," 2020 International Joint Conference on Neural Networks (IJCNN 2020), Online, pp. 1-7, doi: 10.1109/IJCNN48605.2020.9207400, July 2020.
  2. Midori Namba, Kohei Umejima, Ryo Nishide, Takenao Ohkawa, Seiichi Ozawa, Noriyuki Murakami, Hiroyuki Tsuji, "Optimal Pattern Discovery to Reveal the High Yield Inhibition Factor of Soybeans," Journal of the Institute of Industrial Applications Engineers, Vol. 6, No. 2, pp. 66-72, January 2018.
  3. Midori Namba, Kohei Umejima, Ryo Nishide, Takenao Ohkawa, Seiichi Ozawa, Noriyuki Murakami, Hiroyuki Tsuji, "Optimal Pattern Discovery based on Cultivation Data for Elucidation of High Yield Inhibition Factor of Soybean," Proceedings of the 5th IIAE International Conference on Intelligent Systems and Image Processing 2017 (7 pages)
  4. So Yahata, Tetsu Onishi, Kanta Yamaguchi}, Seiichi Ozawa}, Jun Kitazono, Takenao Ohkawa}, Takeshi Yoshida}, Noriyuki Murakami, and Hiroyuki Tsuji, "A Hybrid Machine Learning Approach to Automatic Plant Phenotyping for Smart Agriculture," Proc. of 2017 International Joint Conference on Neural Networks, pp. 1787-1793, May 2017.
  5. Kohei Umejima, Fumihito Arimitsu, Seiichi Ozawa, Noriyuki Murakami, Hiroyuki Tsuji, Takenao Ohkawa, “Optimal Pattern Mining from Time-Series Cultivation Data of Soybeans for Knowledge Discovery,” Proc. of Workshop on Time Series Analytics and Applications, pp 19-24，December 2016.
  6. Shuhei Arakawa, Takeshi Yoshida, Seiichi Ozawa, Takanori Fukao, Takenao Ohkawa, Noriyuki Murakami, and Hiroyuki Tsuji, "A Non-Destructive Measurement Method for Agricultural Plants Using Image Sensing," Proc. of 17th Int.l Symposium on Applied Electromagnetics and Mechanics (ISEM), (in press)

Machine Learning

  1. Nicoleta Rogovschi, Nistor Grozavu,Youn`es Bennani, Seiichi Ozawa, "t-Distributed Stochastic Neighbor Embedding based Self Organizing Maps," 61st ISI World Statistics Congress (ISI2017-Marrakech), July 2017 (6 pages).
  2. Nicoleta Rogovschi, Jun Kitazono, Nistor Grozavu, Toshiaki Omori and Seiichi Ozawa, "t-Distributed Stochastic Neighbor Embedding Spectral Clustering," Proc. of 2017 International Joint Conference on Neural Networks, pp. 1628-1632, May 2017.
  3. Naoki Murata, Jun Kitazono, Seiichi Ozawa, “Multidimensional Unfolding Based on Stochastic Neighbor Relationship,” Proc. of the 9th International Conference on Machine Learning and Computing, pp. 1-5，February 2017.
  4. Jun Kitazono, Nistor Grozavu, Nicoleta Rogovschi, Toshiaki Omori, Seiichi Ozawa, “t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom,” Neural Information Processing: 23rd International Conference, ICONIP 2016, Part III, LNCS vol. 9949, pp 119-128，October 2016.
  5. Aminah Ali Siti Hajar, Kiminori Fukase, and Seiichi Ozawa, "A Neural Network Model for Large-Scale Stream Data Learning Using Locally Sensitive Hashing," Neural Information Processing, Part I, LNCS 8226, pp 369-376, Nov. 2013.
  6. Seiichi Ozawa, Toshihisa Tabuchi, Sho Nakasaka, Asim Roy, "An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks," Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 4, pp. 179-189 (2010.12)
  7. Seiichi Ozawa, Sho Nakasaka, and Asim Roy, "An Autonomous Incremental Learning Algorithm of Resource Allocating Network for Online Pattern Recognition,"Proc. World Congress on Computational Intelligence 2010 (IJCNN) (WCCI2010-Varcelona, Spain), pp. 706-713 (2010.7)
  8. Toshihisa Tabuch, Seiichi Ozawa, and Asim Roy, "An Autonomous Learning Algorithm of Resource Allocating Network," In E. Corchado and H. Yin (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2009, LNCS, Springer, Vol. 5788, pp. 134-141 (2009.10) [Proc. 10th Int. Joint Conf. on Intelligent Data Engineering and Automated Learining 2009 (IDEAL2009-Burgos, Spain)]]

Multitask Learning

  1. Daisuke Higuchi and Seiichi Ozawa, "A Neural Network Model for Semi-supervised Sequential Multi-task Learning in Multi-label Pattern Recognition Problems," in R. Neves-Silva et al. (Eds.), Smart Digital Futures 2014, pp 402-411, June 2014.
  2. Daisuke Higuchi and Seiichi Ozawa, "A Neural Network Model for Online Multi-Task Multi-Label Pattern Recognition," in V. Mladenov et al. (Eds.):Artificial Neural Networks and Machine Learning – ICANN 2013, LNCS 8131, pp 162-169, Sep. 2013.
  3. Tomoyasu Takata, Daisuke Higuchi, and Seiichi Ozawa, “A Sequential Multitask Learning Algorithm for Pattern Recognition,” Proc. IEEE Int. Conf. on Development and Learning and Epigenetic Robotics (ICDL2012, San Diego), pp. 1-2, Nov. 2012.
  4. Simeng Yue and Seiichi Ozawa, A Sequential Multi-task Learning Neural Network with Metric-Based Knowledge Transfer,” Proc. 11th Int. Conf. on Machine Learning and Applications (ICMLA2012, Boca Raton), pp. 671-674, Dec. 2012.
  5. Tomoyasu Takata and Seiichi Ozawa, "A Neural Network Model for Learning Data Stream with Multiple Class Labels," Proc. 10th Int. Conf. on Machine Learning and Applications (ICMLA2011, Honolulu), pp. 35-40 (2011.12)
  6. Hitoshi Nishikawa and Seiichi Ozawa, "Radial Basis Function Network for Multitask Pattern Recognition," Neural Processing Letters, Vol. 33, Issue 3, pp.283-299 (2011.6)
  7. Masayuki Hisada, Seiichi Ozawa, Kau Zhang, and Nikola Kasabov, "Incremental Linear Discriminant Analysis for Evolving Feature Spaces in Multitask Pattern Recognition Problems," Evolving Systems, Springer, Vol. 1, No. 1, pp. 17-27 (2010.8)
  8. Seiichi Ozawa, Asim Roy, and Dmitri Roussinov, "A Multitask Learning Model for Online Pattern Recognition," IEEE Trans. on Neural Networks, Vol. 20, No. 3, pp. 430-445 (2009.3)
  9. Hitoshi Nishikawa, Seiichi Ozawa, and Asim Roy, "A Neural Network Model for Sequential Multitask Pattern Recognition Problems," in Advances in Neuro-Information Processing, Koppen, Mario; Kasabov, Nikola; Coghill, George (Eds.), Lecture Notes in Computer Science Vol. 5506, Part I, Springer, pp. 821-828 [Proc. of 15th Int. Conf. on Neural Information Processing 2008 (ICONIP2008-Auckland, New Zealand)]
  10. Masayuki Hisada, Seiichi Ozawa, Kau Zhang, Shaoning Pang, and Nikola Kasabov, "A Novel Incremental Linear Discriminant Analysis for Multitask Pattern Recognition Problems," in Advances in Neuro-Information Processing, Koppen, Mario; Kasabov, Nikola; Coghill, George (Eds.), Lecture Notes in Computer Science Vol. 5506, Part I, Springer, pp. 1163-1171 [Proc. of 15th Int. Conf. on Neural Information Processing 2008 (ICONIP2008-Auckland, New Zealand)]
  11. Seiichi Ozawa and Asim Roy, "Incremental Learning for Multitask Pattern Recognition Problems," Proc. of 7th Int. Conf. on Machine Learning and Applications (ICMLA2008-San Diego, CA), pp. 747- 751 (2008.12)

(plus 1 Japanese journal paper)

Online Feature Extraction (IPCA, ILDA, IKPCA, IRFLD)

  1. Annie Anak Joseph, Takaomi Tokumoto, Seiichi Ozawa, "Online Feature Extraction based on Accelerated Kernel Principal Component Analysis for Data Stream," Evolving Systems, Mar 2015.
  2. Annie anak Joseph and Seiichi Ozawa, "A Fast Incremental Kernel Principal Component Analysis for Data Streams," Proc. of Int. Joint Conf. on Neural Networks 2014 (IJCNN2014-Beijing), pp. 3135-3142, July 2014.
  3. Y. Choi, S. Ozawa, and M. Lee, "Incremental Two-dimensional Kernel Principal Component Analysis," Neurocomputing, Vol. 134, pp. 280-288, June 2014.
  4. A. A. Joseph, Y.-M. Jang, S. Ozawa, and M. Lee, "An Incremental Linear Discriminant Analysis for Data Streams under Non-stationary Environment," Trans. of Institute of Systems, Control and Information Engineers, Vol. 27, No. 4, pp. 133-140, Apr. 2014.
  5. Daijiro Aoki, Toshiaki Omori, and Seiichi Ozawa, "A Robust Incremental Principal Component Analysis for Feature Extraction from Stream Data with Missing Values," Proc.of Int. Joint Conf. on Neural Networks 2013 (IJCNN2013, Dallas, TX), pp. 1-8, Aug. 2013.
  6. Annie anak Joseph, Young-Ming Jang, Seiichi Ozawa, and Minho Lee, “Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments,” in T. Huang et al. (Eds.): ICONIP 2012, Part II, LNCS 7664, Springer, pp. 640–647, Nov. 2012. [Proc. of 19th Int. Conf. on Neural Information Processing 2012 (ICONIP2012-Doha, Qatar)]
  7. Takaomi Tokumoto and Seiichi Ozawa, “A Property of Learning Chunk Data Using Incremental Kernel Principal Component Analysis,” Proc. IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS2012, Madrid), pp. 7-10, Apr. 2012.
  8. Takaomi Tokumoto and Seiichi Ozawa, "A Fast Incremental Kernel Principal Component Analysis for Learning Stream of Data Chunks," Proc. Int. Joint Conf. on Neural Networks 2011 (IJCNN2011, San Jose), pp. 2881-2888 (2011.8)
  9. Chunyu Liu, Young-Min Jang, Seiichi Ozawa, and Minho Lee, "Incremental 2-Directional 2-Dimensional Linear Discriminant Analysis for Multitask Pattern Recognition," Proc. Int. Joint Conf. on Neural Networks 2011 (IJCNN2011, San Jose), pp. 2911-2916 (2011.8)
  10. Yonghwa Choi, Takaomi Tokumoto, Minho Lee, and Seiichi Ozawa, "Incremental Two-dimensional Two-directional Principal Component Analysis for Face Recognition," Proc. Int. Conf. on Acoustics, Speech and Signal Processing 2011 (ICASSP2011, Prague, Czech Republic), pp. 1493 - 1496 (2011.5)
  11. Seiichi Ozawa and Ryohei Ohta, "Incremental Recursive Fisher Linear Discriminant for Online Feature Extraction," Proc. IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS2011, Paris), pp. 70-76, (2011.4)
  12. Seiichi Ozawa, Yohei Takeuchi, and Shigeo Abe, "A Fast Incremental Kernel Principal Component Analysis for Online Feature Extraction," in PRICAI 2010: Trends in Artificial Intelligence, 11th Pacific Rim International Conference on Artificial Intelligence, Byoung-Tak Zhang and Mehmet A. Orgun (Eds.), Lecture Notes in Artificial Intelligence, Springer, pp. 487-497 (2010.8)
  13. Seiichi Ozawa, Shaoning. Pang, and Nikola Kasabov, "Online Feature Extraction for Evolving Intelligent Systems," in Evolving Intelligent Systems: Methodology and Applications, Plamen Angelov, Dimitar P. Filev, Nik Kasabov, Eds, Wiley-IEEE Press, pp. 151-172 (2010.3.22)
  14. Ryohei Ohta and Seiichi Ozawa, "An Incremental Learning Algorithm of Recursive Fisher Linear Discriminant," Proc. Int. Joint Conf. on Neural Networks 2009 (IJCNN2009-Atlanta, GA), pp. 2310-2315 (2009.6)
  15. Seiichi Ozawa, Yuki Kawashima, Shaoning Pang and Kasabov Nikola, "Adaptive Incremental Principal Component Analysis in Nonstationary Online Learning Environments," Proc. Int. Joint Conf. on Neural Networks 2009 (IJCNN2009-Atlanta, GA), pp. 2394-2400 (2009.6)
  16. Shaoning Pang, Seiichi Ozawa and Nik Kasabov, "Curiosity Driven Incremental LDA Agent Active Learning," Proc. Int. Joint Conf. on Neural Networks 2009 (IJCNN2009-Atlanta, GA), pp. 2401-2408 (2009.6)
  17. Seiichi Ozawa, Kazuya Matsumoto, Shaoning Pang, and Nikola Kasabov, "Incremental Principal Component Analysis Based on Adaptive Accumulation Ratio," in Advances in Neuro-Information Processing, Koppen, Mario; Kasabov, Nikola; Coghill, George (Eds.), Lecture Notes in Computer Science Vol. 5506, Part I, Springer, pp. 1196-1203 [Proc. of 15th Int. Conf. on Neural Information Processing 2008 (ICONIP2008-Auckland, New Zealand)]
  18. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "Incremental Learning of Chunk Data for On-line Pattern Classification Systems," IEEE Trans. on Neural Networks, Vol. 19, No. 6, pp. 1061-1074 (2008.6)
  19. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "Adaptive Face Recognition System Using Fast Incremental Principal Component Analysis," in Neural Information Processing, Lecture Notes in Computer Science Vol. 4985, Springer, pp. 396-405 (2008.6.29) [Proc. of 14th Int. Conf. on Neural Information Processing 2007 (ICONIP2007-Kitakyushu)]
  20. Yohei Takeuchi, Seiichi Ozawa, and Shigeo Abe, "An Efficient Incremental Kernel Principal Component Analysis for Online Feature Selection,'' Proc. Int. Joint Conf. on Neural Networks 2007 (IJCNN2007-Orlando, FL), pp. 1603-1608, (2007.8)
  21. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "An Incremental Principal Component Analysis for Chunk Data,'' Proc. World Congress on Computational Intelligence 2006 (WCCI2006- Vancouver, CA), pp. 10493-10500 (2006.7)
  22. Shosuke Kimura, Seiichi Ozawa, and Shigeo Abe, "Incremental Kernel PCA for Online Learning of Feature Space," Proc. Int. Conf. on Computational Intelligence for Modeling Control and Automation (CIMCA2005), Vol. 1, pp. 595-600 (2005.11)
  23. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "On-line Feature Selection for Adaptive Evolving Connectionist Systems," International Journal of Innovative Computing, Information and Control, Vol. 2, No. 1, pp. 181-192 (2006.2)
  24. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "Incremental Learning of Feature Space and Classifier for On-Line Pattern Recognition," International Journal of Knowledge-Based & Intelligent Engineering Systems, Vol. 10, No. 1, pp. 57-65 (2006.1)
  25. Shaoning Pang, Seiichi Ozawa, and Nikola Kasabov, "Incremental Linear Discriminant Analysis for Classification of Data Streams," IEEE Trans. on Systems, Man, and Cybernetics - Part B. Vol. 35, No. 5, pp. 905-914 (2005.10)
  26. Shaoning Pang, Seiichi Ozawa, and Nikola Kasabov, "Chunk Incremental LDA Computing on Data Streams," in Advances in Neural Networks - ISNN, Wang, Jun; Liao, Xiaofeng; Yi, Zhang (Eds.) Lecture Notes in Computer Science 3497, Springer-Verlag, pp. 51-56 (2005.5) [Proc. of Second Int. Symposium on Neural Networks 2005 (ISNN2005)]
  27. Seiichi Ozawa, Shaoning Pang, and Nikola Kasabov, "A Modified Incremental Principal Component Analysis for On-line Learning of Feature Space and Classifier," in PRICAI 2004: Trends in Artificial Intelligence, C. Zhang, H. W. Guesgen, and W. K. Yeap (Eds.), Lecture Notes in Artificial Intelligence, Springer-Verlag, pp. 231-240 (2004.8) [Proc. of The Pacific Rim International Conferences on Artificial Intelligence (PRICAI2004-Auckland, NZ)]
  28. Shaoning Pang, Seiichi Ozawa, and Nikola Kasabov, "One-pass Incremental Membership Authentication by Face Classification,'' in Biometric Authentication, D. Zhang and A. K. Jain (Eds.), Lecture Notes in Computer Science, Springer-Verlag, pp. 155-161 (2004.6)

(plus 1 Japanese journal paper)

Incremental Learning

  1. Young-Min Jang, Minho Lee, and Seiichi Ozawa, "A Real-time Personal Authentication System Based on Incremental Feature Extraction and Classification of Audiovisual Information, Evolving Systems, Springer, Vol. 2, No. 4, pp. 261-272 (2011.12)
  2. Young-Min Jang, Seiichi Ozawa, and Minho Lee, "A Real-time Personal Authentication System with Selective Attention and Incremental Learning Mechanism in Feature Extraction and Classifier," in PRICAI 2010: Trends in Artificial Intelligence, 11th Pacific Rim International Conference on Artificial Intelligence, Byoung-Tak Zhang and Mehmet A. Orgun (Eds.), Lecture Notes in Artificial Intelligence, Springer, pp. 445-455 (2020.8)
  3. Seiichi Ozawa and Keisuke Okamoto, "An Incremental Learning Algorithm for Resource Allocating Networks Based on Local Linear Regression," in Neuro-Information Processing, Lecture Notes in Computer Science, Springer, Vol. 5863, pp.562-569 (2009.12.15) [Proc. of 15th Int. Conf. on Neural Information Processing 2009 (ICONIP2009-Bangkok, Thailand)]
  4. Seiichi Ozawa, Shigeo Abe, Shaoning Pang, and Nikola Kasabov, "Online Incremental Face Recognition System Using Eigenface Feature and Neural Classifier," in State of the Art in Face Recognition, Julio Ponce and Adem Karahoca, Eds., IN-TECH, pp. 87-108 (2009.1) [FREE Download of this Book]
  5. Takuya Kidera, Seiichi Ozawa, and Shigeo Abe, "An Incremental Learning Algorithm of Ensemble Classifier Systems,'' Proc. World Congress on Computational Intelligence 2006 (WCCI2006- Vancouver, CA), pp. 6453-6459 (2006.7)
  6. Seiichi Ozawa and Shigeo Abe, "One-pass Incremental Learning by Neural Network with Long-term Memory," in Neural Networks Applications in Information Technology and Web Engineering, E. Wang and N. K. Lee, Eds., Borneo Publishing Co., pp. 218-232 (2005)
  7. Seiichi Ozawa, Soon Lee Toh, Shigeo Abe, Shaoning Pang, and Nikola Kasabov, "Incremental Learning of Feature Space and Classifier for Face Recognition," Neural Networks, Vol. 18, No. 5-6, pp. 575-584 (2005.7-8)
  8. Seiichi Ozawa, Soon L. Toh, Sigeo Abe, Shaoning Pang, and Nikola Kasabov, "Incremental learning for online face recognition," Proc. Int. Joint Conf. on Neural Networks 2005, pp. 3174-3179 (2005.7)
  9. Keisuke Okamoto, Seiichi Ozawa, and Shigeo Abe, "A Fast Incremental Learning Algorithm for Radial Basis Function Networks," Transactions of The Society of Instrument and Control Engineers, Vol. 40, No. 12 (2004.12) (in Japanese)
  10. Seiichi Ozawa and Kenji Tsumori, "A Memory-based Neural Network Model for Efficient Adaptation to Dynamic Environments," Proc. of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2004-Budapest, Hungary), pp. 437-442 (2004.7)
  11. S. L. Toh and S. Ozawa, "A Face Recognition System Using Neural Networks with Incremental Learning Ability,'' Proc. of The 8th Australian and New Zealand Conf. on Intelligent Information Systems (ANZIIS2003-Sydney), pp. 389-394 (2003.12)
  12. Seiichi Ozawa, A Memory-based Learning Approach in Neural Networks Under Dynamic Environments, in Dynamic Systems Approach for Embodiment and Sociality, K. Murase and T. Asakura, Eds., International Series on Advanced Intelligence 6, Advanced Knowledge International, pp. 323-328 (2003.9)
  13. Keisuke Okamoto, Seiichi Ozawa, and Shigeo Abe, "A Fast Incremental Learning Algorithm of RBF Networks with Long-Term Memory,'' Proc. of Int. Conf. on Neural Networks 2003 (IJCNN2003-Portland), pp. 323-328 (2003.7)
  14. Kenji Tsumori and Seiichi Ozawa, "Incremental Learning in Dynamic Environments Using Neural Network with Long-term Memory,'' Proc. of Int. Conf. on Neural Networks 2003 (IJCNN2003-Portland), pp. 2583-2588 (2003.7)
  15. Seiichi Ozawa, "A Memory-Based Learning Approach in Neural Networks Under Dynamic Environments,'' Proc. of the Third Int. Symposium on Human and Artificial Intelligence Systems (HART2002-Fukui), pp. 402-405 (2002.12)
  16. Masataka Kobayashi, Anuar Zamani, Seiichi Ozawa, and Shigeo Abe, "Reducing Computations in Incremental Learning for Feedforward Neural Network with Long-term Memory,'' Proc. of Int. Conf. on Neural Networks 2001 (IJCNN2001-Washington DC), pp. 1989-1994 (2001.7)

(plus 4 Japanese journal papers)

Reinforcement Learning

  1. Makoto Murata and Seiichi Ozawa, "A Reinforcement Learning Model Using Deterministic State-action Sequences,"International Journal of Innovative Computing, Information and Control, Vol. 6, No. 2, pp. 577-590 (2010.2)
  2. Hiroshi Onda and Seiichi Ozawa, "A Reinforcement Learning Model Using Macro-actions in Multi-task Grid-World Problems," Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics 2009 (SMC2009-San Antonio, TX), pp. 3088-3093 (2009.9)
  3. Makoto Murata and Seiichi Ozawa, ``A Memory-based Reinforcement Learning Model Utilizing Macro-Actions,' in Adaptive and Natural Computing Algorithms, B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, and N. C. Steele, Eds., Springer Wien New York, pp. 78-81 (2005.3) [Proc 7th Int. Conference on Adaptive and Natural Computing Algorithm]
  4. Seiichi Ozawa and Shigeo Abe, ''A Memory-based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks,'' in Neural Information Processing: Research and Development, Jagath C. Rajapakse and Lipo Wang, Eds., Springer-Verlag, pp. 238-255 (2004)
  5. Naoto Shiraga and Seiichi Ozawa, "Reinforcement Learning Using Feedforward Neural Network with Memory Mechanism," Transactions of The Society of Instrument and Control Engineers, Vol. 39, No. 12, pp. 1129-1135 (2003.12) (in Japanese)
  6. Seiichi Ozawa and Naoto Shiraga, Reinforcement Learning Using RBF Networks with Memory Mechanism, in Knowledge-Based Intelligent Information and Engineering Systems, V. Palade, R. J. Howlett, and L. Jain, Eds., Lecture Notes in Artificial Intelligence, Springer-Verlag, pp. 1149-1156 (2003.9)
  7. Naoto Shiraga, Seiichi Ozawa, and Shigeo Abe, "A Reinforcement Learning Algorithm for Neural Networks with Incremental Learning Ability,'' Proc. of Int. Conf. on Neural Information Processing 2002 (ICONIP2002-Singapore), Vol. 5, pp. 2566-2570 (2002.11)
  8. Naoto Shiraga, Seiichi Ozawa, and Shigeo Abe, "Learning Action-value Functions Using Neural Networks with Incremental Learning Ability,'' Proc. The Fifth Int. Conf. on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies 2001, Vol. I, pp. 22-26 (2001.9)

(plus 1 Japanese journal paper)

Feature Extraction and Pattern Recognition

  1. Takashi Nagatani, Seiichi Ozawa, and Shigeo Abe, "Fast Variable Selection by Block Addition and Block Deletion," Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 4, pp. 200-211 (2010.12)
  2. Kazuya Morikawa, Seiichi Ozawa, and Shigeo Abe, "Tuning Membership Functions of Kernel Fuzzy Classifiers by Maximizing Margins," Memetic Computing, Vol. 1, Nol. 3, pp. 221-228 (2009.9)
  3. Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, and Shigeo Abe, "A Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Classification," IEICE Trans. on Information and Systems, Vol. E90-D, No. 11, pp. 1853-1863 (2007.11)
  4. Shinji Kita, Seiichi Ozawa, Satoshi Maekawa, and Shigeo Abe, "Boosting Kernel Discriminant Analysis for Pattern Classification," Proc. of Int. Symposium on Intelligent Signal Processing and Communication Systems 2007 (ISPACS2007-Xiamen, China), CD-ROM (4 pages) (2007.11)
  5. Hiroki Takabatake, Manabu Kotani, and Seiichi Ozawa, ``Feature Extraction by Supervised Independent Component Analysis Based on Category Information,'' Trans. of the Institute of Electrical Engineers of Japan, Part C (in press) (in Japanese)
  6. Manabu Kotani and Seiichi Ozawa, ``Feature Extraction Using Independent Components of Each Category,'' Neural Processing Letters, Vol. 22, No. 2, pp. 113-124 (2005.10)
  7. Shuhei Kinugawa, Manabu Kotani, and Seiichi Ozawa, "Feature Extraction by Supervised Independent Component Analysis Based on Category Information," Trans. of the Institute of Electrical Engineers of Japan, Part C, Vol. 125-C, No. 5, pp. 807-812 (2005.5) (in Japanese)
  8. Shinji Kita, Satoshi Maekawa, Seiichi Ozawa, and Shigeo Abe, ``Boosting Kernel Discriminant Analysis with Adaptive Kernel Selection,'' in Adaptive and Natural Computing Algorithms, B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, and N. C. Steele, Eds., Springer Wien New York, pp. 429-432 (2005.3) [Proc 7th Int. Conference on Adaptive and Natural Computing Algorithm]
  9. Manabu Kotani, Hiroki Takabatake, and Seiichi Ozawa, "Supervised Independent Component Analysis with Class Information," in Neural Information Processing, N. R. Pal, N. Kasabov, R. K. Mudi, S. Pal, and S. K. Parui (Eds.), Lecture Notes in Computer Science, Springer-Verlag, pp. 1052-1057 (2004.11) [Proc. of Int. Conf. on Neural Information Processing 2004 (ICONIP2004-Calcutta)]
  10. Yoshinori Sakaguchi, Seiichi Ozawa, and Manabu Kotani, "Feature Extraction Using Supervised Independent Component Analysis by Maximizing Class Distance," Trans. of the Institute of Electrical Engineers of Japan, Part C, Vol. 124-C, No. 1, pp. 157-163 (2004) (in Japanese)
  11. Manabu Kotani, Akinobu Sugiyama, Seiichi Ozawa, "Analysis of DNA Microarray Data Using Self-organizing Map and Kernel Based Clustering,'' Proc. of Int. Conf. on Neural Information Processing 2002 (ICONIP2002-Singapore), Vol. 2, pp. 755-759 (2002.11)
  12. Yoshinori Sakaguchi, Seiichi Ozawa, and Manabu Kotani, "Feature Extraction Using Supervised Independent Component Analysis by Maximizing Class Distance,'' Proc. of Int. Conf. on Neural Information Processing 2002 (ICONIP2002-Singapore), Vol. 5, pp. 2502-2506, (2002.11)
  13. Manabu Kotani and Seiichi Ozawa, "A Study on Handwritten Digits Recognition Using Independent Components,'' Proc. of Int. Conf. on Neural Information Processing 2001 (ICONIP2001-Shanghai, China), Vol. III, pp. 1620-1625 (2001.11)
  14. Seiichi Ozawa, Yoshinori Sakaguchi, and Manabu Kotani, "Feature Extraction of Handwritten Characters Using Supervised and Unsupervised Independent Component Analysis,'' Proc. of 5th World Multiconference on Systemics, Cybernetics and Informatics (SCI2001-Orlando), Vol. VI, pp. 151-156 (2001.7)
  15. Seiichi Ozawa, Yoshinori Sakaguchi, and Manabu Kotani, "A Study of Feature Extraction Using Supervised Independent Component Analysis,'' Proc. of Int. Conf. on Neural Networks 2001 (IJCNN2001-Washington DC), pp. 2958-2963 (2001.7)
  16. Manabu Kotani, Takahiko Arimoto, Seiichi Ozawa, and Kenzo Akazawa, "Application of Independent Component Analysis to Detection of Gas Leakage Sound,'' Proc. of Int. Conf. on Neural Networks 2001 (IJCNN2001-Washington DC), pp. 2287-2291 (2001.7)
  17. Seiichi Ozawa and Manabu Kotani, “A Study of Feature Extraction and Selection Using Independent Component Analysis,'' Proc. of Int. Conf. on Neural Information Processing 2000 (ICONIP2000-Taejoｎ, Korea), Vol. I, pp. 369-374 (2000.11)
  18. Manabu Kotani, Seiichi Ozawa, Kenzo Akazawa, and Haruya Matsumoto, ''Detection of Leakage Sound by Using Modular Neural Networks,'' Proc. Sixteenth Congress of the Int. Measurement Confederation 2000, Vol. 4, pp. 347-351 (2000)
  19. Seiichi Ozawa, Toshihide Tsujimoto, Manabu Kotani, and Norio Baba, "Application of Independent Component Analysis to Hand-written Japanese Character Recognition,'' Proc. of international Joint Conf. on Neural Networks (IJCNN99-Washington DC), CD-ROM #462 (1999.7)
  20. Manabu Kotani, Y. Shirata, Satoshi Maekawa, Seiichi Ozawa, Kenzo Akazawa, "Application of Independent Component Analysis to Feature Extraction of Speech,'' Proc. of international Joint Conf. on Neural Networks (IJCNN99-Washington DC), CD-ROM #70 (1999.7)

(plus 4 Japanese journal papers)

Modular Neural Networks and Task Decomposition

  1. Manabu Kotani and Seiichi Ozawa, "Detection of Gas Leakage Sound Using Modular Neural Network for Unknown Environments," Neurocomputing, Vol. 62C, pp. 427-440 (2004.10)
  2. Manabu Kotani, Masanori Katsura, and Seiichi Ozawa, "Application of Modular Neural Networks to Detection of Gas Leakage Under Dynamic Environments,'' Proc. The Fifth Int. Conf. on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies 2001, Vol. I, pp. 308-312 (2001.9)
  3. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "An Artificial Modular Neural Network and Its Basic Dynamical Characteristics,'' Biological Cybernetics, Vol. 78, No. 1, pp. 19-36 (1998)
  4. Seiichi Ozawa and Kazuyoshi Tsutsumi, "A Multi-Module Neural Network and The Estimate of Its Nature as Associative Memory,'' Systems and Computers in Japan, Scripta, Vol. 26, No.1, pp. 99-110 (1995)
  5. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "A Modular Neural Network with Local and Global Interactions,'' Proc. of World Congress on Neural Networks (WCNN95-Washington DC), Vol. I, pp. 471-476 (1995)
  6. S. Ozawa, K. Tsutsumi and N. Baba, "The estimation of Cross-Coupled Hopfield Nets as an interactive modular neural network," Proc. of International Conf. on Neural Networks (ICNN94-Orlando), Vol. III, pp. 1340-1345 (1994)
  7. Seiichi Ozawa, Kazuyoshi Tsutsumi and Haruya Matsumoto, "Basic Dynamical Properties of Cross-Coupled Hopfield Nets,'' Proc. of International Joint Conf. on Neural Networks (IJCNN91-Singapore), Vol. III, pp. 1949-1954 (1991)

(plus 1 Japanese journal paper)

Associative Neural Memories

  1. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "A Continuous-time Model of Autoassociative Neural Memories Utilizing the Noise-subspace Dynamics,'' Neural Processing Letters, Vol. 10, Issue 2, pp. 97-109 (1999)
  2. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "An Associative Memory Model Derived from Cross-Coupled Hopfield Nets and the Role of Noise-space Dynamics," Electrical Engineering in Japan, Vol. 125, No. 2, pp. 27-34 (1998)
  3. Seiichi. Ozawa and Kazuyoshi Tsutsumi, "Association Performance of Cross-Coupled Hopfield Nets for Correlated Patterns,'' Proc. of International Joint Conf. on Neural Networks (IJCNN93-Nagoya), Vol. II, pp. 2335-2338 (1993)
  4. Seiichi Ozawa, Kazuyoshi Tsutsumi and Haruya Matsumoto, "Association Dynamics of Cross-Coupled Hopfield Nets with Many-to-many Mapping Internetworks,'' Artificial Neural Networks 2, Elsevier, pp. 375-378 (1992) (Proc. of Int. Conf. on Artificial Neural Networks, ICANN92-Brighton, UK)
(plus 3 other Japanese journal papers)

Neural Network Learning

  1. Shigeo Abe, Yoichi Hirokawa, and Seiichi Ozawa, "Steepest Ascent Training of Support Vector Machines,'' KES'2002 Sixth International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2002-Crema, Italy), CD-ROM (2002.9)
  2. Naoki Tsuchiya, Seiichi Ozawa, and Shigeo Abe, ''Training Three-layer Neural Network Classifiers by Solving Inequalities,'' Proc. Int. Joint Conf. on Neural Networks 2000 (IJCNN2000-Como, Italy), Vol. III, pp. 555-560 (2000)
  3. Akira Morimoto, Seiichi Ozawa, and Ryuichi Ashino, "An Efficient Identification Method  of the Structural Parameters of  MDOF Structures Using the Wavelet Transform and Neural Networks,'' Proc. of Second World Conference on Structural Control, Vol. III, pp. 2133-2140 (1998)

(plus 2 Japanese journal papers)

Evolutionary Computations

  1. Manabu Kotani, Makoto Ochi, Seiichi Ozawa, and Kenzo Akazawa, "Evolutionary Discriminant Functions Using Genetic Algorithms with Variable-Length Chromosome,'' Proc. of Int. Conf. on Neural Networks 2001 (IJCNN2001-Washington DC), pp. 761-766 (2001.7)
  2. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "Evolution of A Dynamical Modular Neural Network and Its Application to Associative Memories,'' Proc. of 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES99-Adelaide, Australia), pp. 145-148 (1999)
  3. Manabu Kotani, Seiichi Ozawa, Masaki Nakai, and Kenzo Akazawa, "Emergence of Feature Extraction Function Using Genetic Programming,'' Proc. of 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES99-Adelaide, Australia), pp. 149-152 (1999)
  4. Seiichi Ozawa, Kazuyoshi Tsutsumi, and Norio Baba, "Design of Modular Neural Network Architectures Using Genetic Algorithms,'' Proc. of International Conference on Neural Information Processing '98 (ICONIP98-Kitakyushu), Vol. III, pp. 1608-1611 (1998)

(plus 1 other Japanese journal paper)