| 2009 | ||
|---|---|---|
| 74 | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. ACML 2009: 322-337 | |
| 73 | Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Discovering Influential Nodes for SIS Models in Social Networks. Discovery Science 2009: 302-316 | |
| 72 | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Efficient Estimation of Influence Functions for SIS Model on Social Networks. IJCAI 2009: 2046-2051 | |
| 71 | Ken-ichi Fukui, Kazuhisa Sato, Junichiro Mizusaki, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Growth Analysis of Neighbor Network for Evaluation of Damage Progress. PAKDD 2009: 933-940 | |
| 70 | Kazumi Saito, Takeshi Yamada, Kazuhiro Kazama: The k-Dense Method to Extract Communities from Complex Networks. Mining Complex Data 2009: 243-257 | |
| 69 | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Blocking links to minimize contamination spread in a social network. TKDD 3(2): (2009) | |
| 2008 | ||
| 68 | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Minimizing the Spread of Contamination by Blocking Links in a Network. AAAI 2008: 1175-1180 | |
| 67 | Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Effective Visualization of Information Diffusion Process over Complex Networks. ECML/PKDD (2) 2008: 326-341 | |
| 66 | Masahiro Kimura, Kazumasa Yamakawa, Kazumi Saito, Hiroshi Motoda: Community analysis of influential nodes for information diffusion on a social network. IJCNN 2008: 1358-1363 | |
| 65 | Kazumi Saito, Nobuaki Mutoh, Tetsuo Ikeda, Toshinao Goda, Kazuki Mochizuki: Improving Search Efficiency of Incremental Variable Selection by Using Second-Order Optimal Criterion. KES (3) 2008: 41-49 | |
| 64 | Kazumi Saito, Ryohei Nakano, Masahiro Kimura: Prediction of Information Diffusion Probabilities for Independent Cascade Model. KES (3) 2008: 67-75 | |
| 63 | Takayasu Fushimi, Takashi Kawazoe, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: What Does an Information Diffusion Model Tell about Social Network Structure?. PKAW 2008: 122-136 | |
| 62 | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model. PRICAI 2008: 977-984 | |
| 61 | Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Recommendation Method for Improving Customer Lifetime Value. IEEE Trans. Knowl. Data Eng. 20(9): 1254-1263 (2008) | |
| 60 | Akinori Fujino, Naonori Ueda, Kazumi Saito: Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle. IEEE Trans. Pattern Anal. Mach. Intell. 30(3): 424-437 (2008) | |
| 59 | Kazumi Saito, Takeshi Yamada, Kazuhiro Kazama: Extracting Communities from Complex Networks by the k-Dense Method. IEICE Transactions 91-A(11): 3304-3311 (2008) | |
| 2007 | ||
| 58 | Masahiro Kimura, Kazumi Saito, Ryohei Nakano: Extracting Influential Nodes for Information Diffusion on a Social Network. AAAI 2007: 1371-1376 | |
| 57 | Kazumi Saito, Pat Langley: Quantitative Revision of Scientific Models. Computational Discovery of Scientific Knowledge 2007: 120-137 | |
| 56 | Ken-ichi Fukui, Kazuhisa Sato, Junichiro Mizusaki, Kazumi Saito, Masayuki Numao: Combining Burst Extraction Method and Sequence-Based SOM for Evaluation of Fracture Dynamics in Solid Oxide Fuel Cell. ICTAI (2) 2007: 193-196 | |
| 55 | Akinori Fujino, Naonori Ueda, Kazumi Saito: Semi-Supervised Learning for Multi-Component Data Classification. IJCAI 2007: 2754-2759 | |
| 54 | Pablo A. Estévez, Pablo A. Vera, Kazumi Saito: Selecting the Most Influential Nodes in Social Networks. IJCNN 2007: 2397-2402 | |
| 53 | Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Interpretable Likelihood for Vector Representable Topic. KES (3) 2007: 202-209 | |
| 52 | Manabu Kimura, Kazumi Saito, Naonori Ueda: Pivot Learning for Efficient Similarity Search. KES (3) 2007: 227-234 | |
| 51 | Kazumi Saito, Ryohei Nakano, Masahiro Kimura: Prediction of Link Attachments by Estimating Probabilities of Information Propagation. KES (3) 2007: 235-242 | |
| 50 | Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Modeling user behavior in recommender systems based on maximum entropy. WWW 2007: 1281-1282 | |
| 49 | Akinori Fujino, Naonori Ueda, Kazumi Saito: A hybrid generative/discriminative approach to text classification with additional information. Inf. Process. Manage. 43(2): 379-392 (2007) | |
| 48 | Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum: Parametric Embedding for Class Visualization. Neural Computation 19(9): 2536-2556 (2007) | |
| 47 | Kazumi Saito, Ryohei Nakano: Bidirectional clustering of weights for neural networks with common weights. Systems and Computers in Japan 38(10): 46-57 (2007) | |
| 2006 | ||
| 46 | Tomoharu Iwata, Kazumi Saito, Naonori Ueda: Visual nonlinear discriminant analysis for classifier design. ESANN 2006: 283-288 | |
| 45 | Kazumi Saito, Takeshi Yamada: Extracting Communities from Complex Networks by the k-dense Method. ICDM Workshops 2006: 300-304 | |
| 44 | Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Recommendation method for extending subscription periods. KDD 2006: 574-579 | |
| 43 | Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Visualization Architecture Based on SOM for Two-Class Sequential Data. KES (2) 2006: 929-936 | |
| 42 | Masahiro Kimura, Kazumi Saito: Approximate Solutions for the Influence Maximization Problem in a Social Network. KES (2) 2006: 937-944 | |
| 41 | Kazumi Saito, Ryohei Nakano: Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach. KES (2) 2006: 945-952 | |
| 40 | Yusuke Tanahashi, Kazumi Saito, Daisuke Kitakoshi, Ryohei Nakano: Finding Nominally Conditioned Multivariate Polynomials Using a Four-Layer Perceptron Having Shared Weights. KES (2) 2006: 969-976 | |
| 39 | Masahiro Kimura, Kazumi Saito: Tractable Models for Information Diffusion in Social Networks. PKDD 2006: 259-271 | |
| 38 | Naonori Ueda, Kazumi Saito: Parametric mixture model for multitopic text. Systems and Computers in Japan 37(2): 56-66 (2006) | |
| 2005 | ||
| 37 | Akinori Fujino, Naonori Ueda, Kazumi Saito: A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design. AAAI 2005: 764-769 | |
| 36 | Akinori Fujino, Naonori Ueda, Kazumi Saito: A Classifier Design Based on Combining Multiple Components by Maximum Entropy Principle. AIRS 2005: 423-438 | |
| 35 | Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano: Model Selection and Weight Sharing of Multi-layer Perceptrons. KES (4) 2005: 716-722 | |
| 34 | Masahiro Kimura, Kazumi Saito, Kazuhiro Kazama, Shin-ya Sato: Detecting Search Engine Spam from a Trackback Network in Blogspace. KES (4) 2005: 723-729 | |
| 33 | Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Visualizing Dynamics of the Hot Topics Using Sequence-Based Self-organizing Maps. KES (4) 2005: 745-751 | |
| 32 | Pablo A. Estévez, Cristián J. Figueroa, Kazumi Saito: Cross-entropy embedding of high-dimensional data using the neural gas model. Neural Networks 18(5-6): 727-737 (2005) | |
| 2004 | ||
| 31 | Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano: Piecewise Multivariate Polynomials Using a Four-Layer Perceptron. KES 2004: 602-608 | |
| 30 | Yuji Kaneda, Naonori Ueda, Kazumi Saito: Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization. KES 2004: 616-623 | |
| 29 | Tomoharu Iwata, Kazumi Saito: Visualisation of Anomaly Using Mixture Model. KES 2004: 624-631 | |
| 28 | Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum: Parametric Embedding for Class Visualization. NIPS 2004 | |
| 27 | Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling of growing networks with directional attachment and communities. Neural Networks 17(7): 975-988 (2004) | |
| 26 | Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling network growth with directional attachment and communities. Systems and Computers in Japan 35(8): 1-11 (2004) | |
| 2003 | ||
| 25 | Dileep George, Kazumi Saito, Pat Langley, Stephen D. Bay, Kevin R. Arrigo: Discovering Ecosystem Models from Time-Series Data. Discovery Science 2003: 141-152 | |
| 24 | Kazumi Saito, Dileep George, Stephen D. Bay, Jeff Shrager: Inducing Biological Models from Temporal Gene Expression Data. Discovery Science 2003: 468-469 | |
| 23 | Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling of growing networks with directional attachment and communities. ESANN 2003: 15-20 | |
| 22 | Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito: Robust Induction of Process Models from Time-Series Data. ICML 2003: 432-439 | |
| 21 | Takeshi Yamada, Kazumi Saito, Naonori Ueda: Cross-Entropy Directed Embedding of Network Data. ICML 2003: 832-839 | |
| 2002 | ||
| 20 | Kazumi Saito, Ryohei Nakano: Structuring Neural Networks through Bidirectional Clustering of Weights. Discovery Science 2002: 206-219 | |
| 19 | Kazumi Saito, Stephen D. Bay, Pat Langley: Revising Qualitative Models of Gene Regulation. Discovery Science 2002: 59-70 | |
| 18 | Naonori Ueda, Kazumi Saito: Single-shot detection of multiple categories of text using parametric mixture models. KDD 2002: 626-631 | |
| 17 | Naonori Ueda, Kazumi Saito: Parametric Mixture Models for Multi-Labeled Text. NIPS 2002: 721-728 | |
| 16 | Ryohei Nakano, Kazumi Saito: Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. Progress in Discovery Science 2002: 482-493 | |
| 15 | Kazumi Saito, Pat Langley: Discovering Empirical Laws of Web Dynamics. SAINT 2002: 168-175 | |
| 14 | Kazumi Saito, Ryohei Nakano: Extracting regression rules from neural networks. Neural Networks 15(10): 1279-1288 (2002) | |
| 2001 | ||
| 13 | Kazumi Saito, Pat Langley, Trond Grenager, Christopher Potter, Alicia Torregrosa, Steven A. Klooster: Computational Revision of Quantitative Scientific Models. Discovery Science 2001: 336-349 | |
| 12 | Ryohei Nakano, Kazumi Saito: Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. IDA 2001: 258-267 | |
| 2000 | ||
| 11 | Kazumi Saito, Ryohei Nakano: Discovery of Nominally Conditioned Polynomials Using Neural Networks, Vector Quantizers and Decision Trees. Discovery Science 2000: 325-329 | |
| 10 | Kazumi Saito, Ryohei Nakano: Discovery of Relevant Weights by Minimizing Cross-Validation Error. PAKDD 2000: 372-375 | |
| 9 | Kazumi Saito, Ryohei Nakano: Second-Order Learning Algorithm with Squared Penalty Term. Neural Computation 12(3): 709-729 (2000) | |
| 1999 | ||
| 8 | Ryohei Nakano, Kazumi Saito: Discovery of a Set of Nominally Conditioned Polynomials. Discovery Science 1999: 287-298 | |
| 1998 | ||
| 7 | Ryohei Nakano, Kazumi Saito: Computational Characteristics of Law Discovery Using Neural Networks. Discovery Science 1998: 342-351 | |
| 1997 | ||
| 6 | Kazumi Saito, Ryohei Nakano: Law Discovery using Neural Networks. IJCAI 1997: 1078-1083 | |
| 5 | Kazumi Saito, Ryohei Nakano: Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. Neural Computation 9(1): 123-141 (1997) | |
| 1996 | ||
| 4 | Kazumi Saito, Ryohei Nakano: Second-order Learning Algorithm with Squared Penalty Term. NIPS 1996: 627-633 | |
| 1995 | ||
| 3 | Kazumi Saito, Ryohei Nakano: A Connectionist Approach to Numeric Law Discorvery. Machine Intelligence 15 1995: 315-327 | |
| 1994 | ||
| 2 | Kazumi Saito, Ryohei Nakano: Adaptive Concept Learning Algorithm. IFIP Congress (1) 1994: 294-299 | |
| 1993 | ||
| 1 | Kazumi Saito, Ryohei Nakano: A concept learning algorithm with adaptive search. Machine Intelligence 14 1993: 353- | |