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Kazumi Saito
2010 – today
- 2013
[c81]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Which Targets to Contact First to Maximize Influence over Social Network. SBP 2013: 359-367- 2012
- [j20]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Efficient discovery of influential nodes for SIS models in social networks. Knowl. Inf. Syst. 30(3): 613-635 (2012)
- [c80]Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Burst Detection in a Sequence of Tweets Based on Information Diffusion Model. Discovery Science 2012: 239-253
- [c79]Shoko Kato, Akihiro Koide, Takayasu Fushimi, Kazumi Saito, Hiroshi Motoda: Network Analysis of Three Twitter Functions: Favorite, Follow and Mention. PKAW 2012: 298-312
- [c78]Takayasu Fushimi, Kazumi Saito, Kazuhiro Kazama: Extracting Communities in Networks Based on Functional Properties of Nodes. PKAW 2012: 328-334
- [c77]Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Opinion Formation by Voter Model with Temporal Decay Dynamics. ECML/PKDD (2) 2012: 565-580
- [c76]Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Effect of In/Out-Degree Correlation on Influence Degree of Two Contrasting Information Diffusion Models. SBP 2012: 131-138
- [c75]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Graph embedding on spheres and its application to visualization of information diffusion data. WWW (Companion Volume) 2012: 1137-1144
- [i2]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Learning Asynchronous-Time Information Diffusion Models and its Application to Behavioral Data Analysis over Social Networks. CoRR abs/1204.4528 (2012)
- 2011
- [j19]Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Learning information diffusion model in a social network for predicting influence of nodes. Intell. Data Anal. 15(4): 633-652 (2011)
- [j18]Yuki Yamagishi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Learning Attribute-weighted Voter Model over Social Networks. Journal of Machine Learning Research - Proceedings Track 20: 263-280 (2011)
- [j17]Akihiro Koide, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Estimating Diffusion Probability Changes for AsIC-SIS Model. Journal of Machine Learning Research - Proceedings Track 20: 297-313 (2011)
- [c74]Takayasu Fushimi, Yamato Kubota, Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Speeding Up Bipartite Graph Visualization Method. Australasian Conference on Artificial Intelligence 2011: 697-706
- [c73]Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Detecting Anti-majority Opinionists Using Value-Weighted Mixture Voter Model. Discovery Science 2011: 150-164
- [c72]Kazumi Saito, Kouzou Ohara, Yuki Yamagishi, Masahiro Kimura, Hiroshi Motoda: Learning Diffusion Probability Based on Node Attributes in Social Networks. ISMIS 2011: 153-162
- [c71]Kazuo Aoyama, Kazumi Saito, Hiroshi Sawada, Naonori Ueda: Fast approximate similarity search based on degree-reduced neighborhood graphs. KDD 2011: 1055-1063
- [c70]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Detecting Changes in Opinion Value Distribution for Voter Model. SBP 2011: 89-96
- [i1]Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Efficient Detection of Hot Span in Information Diffusion from Observation. CoRR abs/1110.2659 (2011)
- 2010
- [j16]Masahiro Kimura, Kazumi Saito, Ryohei Nakano, Hiroshi Motoda: Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Discov. 20(1): 70-97 (2010)
- [j15]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Generative Models of Information Diffusion with Asynchronous Timedelay. Journal of Machine Learning Research - Proceedings Track 13: 193-208 (2010)
- [c69]Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Learning to Predict Opinion Share in Social Networks. AAAI 2010
- [c68]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Discovery of Super-Mediators of Information Diffusion in Social Networks. Discovery Science 2010: 144-158
- [c67]Yusuke Tanahashi, Ryohei Nakano, Kazumi Saito: Nominally Conditioned Linear Regression. ICANN (3) 2010: 290-293
- [c66]Kazuo Aoyama, Shinji Watanabe, Hiroshi Sawada, Yasuhiro Minami, Naonori Ueda, Kazumi Saito: Fast similarity search on a large speech data set with neighborhood graph indexing. ICASSP 2010: 5358-5361
- [c65]Takayasu Fushimi, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda, Kouzou Ohara: Finding Relation between PageRank and Voter Model. PKAW 2010: 208-222
- [c64]Yuya Yoshikawa, Kazumi Saito, Hiroshi Motoda, Kouzou Ohara, Masahiro Kimura: Acquiring Expected Influence Curve from Single Diffusion Sequence. PKAW 2010: 273-287
- [c63]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Selecting Information Diffusion Models over Social Networks for Behavioral Analysis. ECML/PKDD (3) 2010: 180-195
- [c62]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Efficient Estimation of Cumulative Influence for Multiple Activation Information Diffusion Model with Continuous Time Delay. PRICAI 2010: 244-255
- [c61]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network. SBP 2010: 149-158
2000 – 2009
- 2009
- [j14]Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Blocking links to minimize contamination spread in a social network. TKDD 3(2) (2009)
- [c60]Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. ACML 2009: 322-337
- [c59]Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Discovering Influential Nodes for SIS Models in Social Networks. Discovery Science 2009: 302-316
- [c58]Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Efficient Estimation of Influence Functions for SIS Model on Social Networks. IJCAI 2009: 2046-2051
- [c57]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
- [p1]Kazumi Saito, Takeshi Yamada, Kazuhiro Kazama: The k-Dense Method to Extract Communities from Complex Networks. Mining Complex Data 2009: 243-257
- 2008
- [j13]Kazumi Saito, Takeshi Yamada, Kazuhiro Kazama: Extracting Communities from Complex Networks by the k-Dense Method. IEICE Transactions 91-A(11): 3304-3311 (2008)
- [j12]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)
- [j11]Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Recommendation Method for Improving Customer Lifetime Value. IEEE Trans. Knowl. Data Eng. 20(9): 1254-1263 (2008)
- [c56]Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Sequence-based SOM: Visualizing transition of dynamic clusters. CIT 2008: 47-52
- [c55]Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Minimizing the Spread of Contamination by Blocking Links in a Network. AAAI 2008: 1175-1180
- [c54]Masahiro Kimura, Kazumasa Yamakawa, Kazumi Saito, Hiroshi Motoda: Community analysis of influential nodes for information diffusion on a social network. IJCNN 2008: 1358-1363
- [c53]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
- [c52]Kazumi Saito, Ryohei Nakano, Masahiro Kimura: Prediction of Information Diffusion Probabilities for Independent Cascade Model. KES (3) 2008: 67-75
- [c51]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
- [c50]Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Effective Visualization of Information Diffusion Process over Complex Networks. ECML/PKDD (2) 2008: 326-341
- [c49]Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model. PRICAI 2008: 977-984
- 2007
- [j10]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)
- [j9]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)
- [j8]Kazumi Saito, Ryohei Nakano: Bidirectional clustering of weights for neural networks with common weights. Systems and Computers in Japan 38(10): 46-57 (2007)
- [c48]Masahiro Kimura, Kazumi Saito, Ryohei Nakano: Extracting Influential Nodes for Information Diffusion on a Social Network. AAAI 2007: 1371-1376
- [c47]Kazumi Saito, Pat Langley: Quantitative Revision of Scientific Models. Computational Discovery of Scientific Knowledge 2007: 120-137
- [c46]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
- [c45]Akinori Fujino, Naonori Ueda, Kazumi Saito: Semi-Supervised Learning for Multi-Component Data Classification. IJCAI 2007: 2754-2759
- [c44]Pablo A. Estévez, Pablo A. Vera, Kazumi Saito: Selecting the Most Influential Nodes in Social Networks. IJCNN 2007: 2397-2402
- [c43]Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Interpretable Likelihood for Vector Representable Topic. KES (3) 2007: 202-209
- [c42]Manabu Kimura, Kazumi Saito, Naonori Ueda: Pivot Learning for Efficient Similarity Search. KES (3) 2007: 227-234
- [c41]Kazumi Saito, Ryohei Nakano, Masahiro Kimura: Prediction of Link Attachments by Estimating Probabilities of Information Propagation. KES (3) 2007: 235-242
- [c40]Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Modeling user behavior in recommender systems based on maximum entropy. WWW 2007: 1281-1282
- 2006
- [j7]Naonori Ueda, Kazumi Saito: Parametric mixture model for multitopic text. Systems and Computers in Japan 37(2): 56-66 (2006)
- [c39]Tomoharu Iwata, Kazumi Saito, Naonori Ueda: Visual nonlinear discriminant analysis for classifier design. ESANN 2006: 283-288
- [c38]Kazumi Saito, Takeshi Yamada: Extracting Communities from Complex Networks by the k-dense Method. ICDM Workshops 2006: 300-304
- [c37]Tomoharu Iwata, Kazumi Saito, Takeshi Yamada: Recommendation method for extending subscription periods. KDD 2006: 574-579
- [c36]Ken-ichi Fukui, Kazumi Saito, Masahiro Kimura, Masayuki Numao: Visualization Architecture Based on SOM for Two-Class Sequential Data. KES (2) 2006: 929-936
- [c35]Masahiro Kimura, Kazumi Saito: Approximate Solutions for the Influence Maximization Problem in a Social Network. KES (2) 2006: 937-944
- [c34]Kazumi Saito, Ryohei Nakano: Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach. KES (2) 2006: 945-952
- [c33]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
- [c32]Masahiro Kimura, Kazumi Saito: Tractable Models for Information Diffusion in Social Networks. PKDD 2006: 259-271
- 2005
- [j6]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)
- [c31]Akinori Fujino, Naonori Ueda, Kazumi Saito: A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design. AAAI 2005: 764-769
- [c30]Akinori Fujino, Naonori Ueda, Kazumi Saito: A Classifier Design Based on Combining Multiple Components by Maximum Entropy Principle. AIRS 2005: 423-438
- [c29]Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano: Model Selection and Weight Sharing of Multi-layer Perceptrons. KES (4) 2005: 716-722
- [c28]Masahiro Kimura, Kazumi Saito, Kazuhiro Kazama, Shin-ya Sato: Detecting Search Engine Spam from a Trackback Network in Blogspace. KES (4) 2005: 723-729
- [c27]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
- 2004
- [j5]Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling of growing networks with directional attachment and communities. Neural Networks 17(7): 975-988 (2004)
- [j4]Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling network growth with directional attachment and communities. Systems and Computers in Japan 35(8): 1-11 (2004)
- [c26]Yusuke Tanahashi, Kazumi Saito, Ryohei Nakano: Piecewise Multivariate Polynomials Using a Four-Layer Perceptron. KES 2004: 602-608
- [c25]Yuji Kaneda, Naonori Ueda, Kazumi Saito: Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization. KES 2004: 616-623
- [c24]
- [c23]Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum: Parametric Embedding for Class Visualization. NIPS 2004
- 2003
- [c22]Dileep George, Kazumi Saito, Pat Langley, Stephen D. Bay, Kevin R. Arrigo: Discovering Ecosystem Models from Time-Series Data. Discovery Science 2003: 141-152
- [c21]Kazumi Saito, Dileep George, Stephen D. Bay, Jeff Shrager: Inducing Biological Models from Temporal Gene Expression Data. Discovery Science 2003: 468-469
- [c20]Masahiro Kimura, Kazumi Saito, Naonori Ueda: Modeling of growing networks with directional attachment and communities. ESANN 2003: 15-20
- [c19]Pat Langley, Dileep George, Stephen D. Bay, Kazumi Saito: Robust Induction of Process Models from Time-Series Data. ICML 2003: 432-439
- [c18]Takeshi Yamada, Kazumi Saito, Naonori Ueda: Cross-Entropy Directed Embedding of Network Data. ICML 2003: 832-839
- 2002
- [j3]Kazumi Saito, Ryohei Nakano: Extracting regression rules from neural networks. Neural Networks 15(10): 1279-1288 (2002)
- [c17]Kazumi Saito, Stephen D. Bay, Pat Langley: Revising Qualitative Models of Gene Regulation. Discovery Science 2002: 59-70
- [c16]Kazumi Saito, Ryohei Nakano: Structuring Neural Networks through Bidirectional Clustering of Weights. Discovery Science 2002: 206-219
- [c15]Ryohei Nakano, Kazumi Saito: Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. Progress in Discovery Science 2002: 482-493
- [c14]Naonori Ueda, Kazumi Saito: Single-shot detection of multiple categories of text using parametric mixture models. KDD 2002: 626-631
- [c13]
- [c12]
- 2001
- [c11]Kazumi Saito, Pat Langley, Trond Grenager, Christopher Potter, Alicia Torregrosa, Steven A. Klooster: Computational Revision of Quantitative Scientific Models. Discovery Science 2001: 336-349
- [c10]Ryohei Nakano, Kazumi Saito: Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables. IDA 2001: 258-267
- 2000
- [j2]Kazumi Saito, Ryohei Nakano: Second-Order Learning Algorithm with Squared Penalty Term. Neural Computation 12(3): 709-729 (2000)
- [c9]Kazumi Saito, Ryohei Nakano: Discovery of Nominally Conditioned Polynomials Using Neural Networks, Vector Quantizers and Decision Trees. Discovery Science 2000: 325-329
- [c8]Kazumi Saito, Ryohei Nakano: Discovery of Relevant Weights by Minimizing Cross-Validation Error. PAKDD 2000: 372-375
1990 – 1999
- 1999
- [c7]Ryohei Nakano, Kazumi Saito: Discovery of a Set of Nominally Conditioned Polynomials. Discovery Science 1999: 287-298
- 1998
- [c6]Ryohei Nakano, Kazumi Saito: Computational Characteristics of Law Discovery Using Neural Networks. Discovery Science 1998: 342-351
- 1997
- [j1]Kazumi Saito, Ryohei Nakano: Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. Neural Computation 9(1): 123-141 (1997)
- [c5]
- 1996
- [c4]Kazumi Saito, Ryohei Nakano: Second-order Learning Algorithm with Squared Penalty Term. NIPS 1996: 627-633
- 1995
- [c3]Kazumi Saito, Ryohei Nakano: A Connectionist Approach to Numeric Law Discorvery. Machine Intelligence 15 1995: 315-327
- 1994
- [c2]
- 1993
- [c1]Kazumi Saito, Ryohei Nakano: A concept learning algorithm with adaptive search. Machine Intelligence 14 1993: 353-
Coauthor Index
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last updated on 2013-03-05 19:38 CET by the dblp team