| 2009 |
|---|
| 69 | EE | 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 |
| 68 | EE | Kazumi Saito,
Takeshi Yamada,
Kazuhiro Kazama:
The k-Dense Method to Extract Communities from Complex Networks.
Mining Complex Data 2009: 243-257 |
| 67 | EE | Masahiro Kimura,
Kazumi Saito,
Hiroshi Motoda:
Blocking links to minimize contamination spread in a social network.
TKDD 3(2): (2009) |
| 2008 |
|---|
| 66 | | Masahiro Kimura,
Kazumi Saito,
Hiroshi Motoda:
Minimizing the Spread of Contamination by Blocking Links in a Network.
AAAI 2008: 1175-1180 |
| 65 | EE | Kazumi Saito,
Masahiro Kimura,
Hiroshi Motoda:
Effective Visualization of Information Diffusion Process over Complex Networks.
ECML/PKDD (2) 2008: 326-341 |
| 64 | EE | Masahiro Kimura,
Kazumasa Yamakawa,
Kazumi Saito,
Hiroshi Motoda:
Community analysis of influential nodes for information diffusion on a social network.
IJCNN 2008: 1358-1363 |
| 63 | EE | 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 |
| 62 | EE | Kazumi Saito,
Ryohei Nakano,
Masahiro Kimura:
Prediction of Information Diffusion Probabilities for Independent Cascade Model.
KES (3) 2008: 67-75 |
| 61 | EE | Masahiro Kimura,
Kazumi Saito,
Hiroshi Motoda:
Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model.
PRICAI 2008: 977-984 |
| 60 | EE | Tomoharu Iwata,
Kazumi Saito,
Takeshi Yamada:
Recommendation Method for Improving Customer Lifetime Value.
IEEE Trans. Knowl. Data Eng. 20(9): 1254-1263 (2008) |
| 59 | EE | 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) |
| 2007 |
|---|
| 58 | | Masahiro Kimura,
Kazumi Saito,
Ryohei Nakano:
Extracting Influential Nodes for Information Diffusion on a Social Network.
AAAI 2007: 1371-1376 |
| 57 | EE | Kazumi Saito,
Pat Langley:
Quantitative Revision of Scientific Models.
Computational Discovery of Scientific Knowledge 2007: 120-137 |
| 56 | EE | 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 | EE | Akinori Fujino,
Naonori Ueda,
Kazumi Saito:
Semi-Supervised Learning for Multi-Component Data Classification.
IJCAI 2007: 2754-2759 |
| 54 | EE | Pablo A. Estévez,
Pablo A. Vera,
Kazumi Saito:
Selecting the Most Influential Nodes in Social Networks.
IJCNN 2007: 2397-2402 |
| 53 | EE | Ken-ichi Fukui,
Kazumi Saito,
Masahiro Kimura,
Masayuki Numao:
Interpretable Likelihood for Vector Representable Topic.
KES (3) 2007: 202-209 |
| 52 | EE | Manabu Kimura,
Kazumi Saito,
Naonori Ueda:
Pivot Learning for Efficient Similarity Search.
KES (3) 2007: 227-234 |
| 51 | EE | Kazumi Saito,
Ryohei Nakano,
Masahiro Kimura:
Prediction of Link Attachments by Estimating Probabilities of Information Propagation.
KES (3) 2007: 235-242 |
| 50 | EE | Tomoharu Iwata,
Kazumi Saito,
Takeshi Yamada:
Modeling user behavior in recommender systems based on maximum entropy.
WWW 2007: 1281-1282 |
| 49 | EE | 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 | EE | 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 | EE | 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 | EE | Tomoharu Iwata,
Kazumi Saito,
Naonori Ueda:
Visual nonlinear discriminant analysis for classifier design.
ESANN 2006: 283-288 |
| 45 | EE | Kazumi Saito,
Takeshi Yamada:
Extracting Communities from Complex Networks by the k-dense Method.
ICDM Workshops 2006: 300-304 |
| 44 | EE | Tomoharu Iwata,
Kazumi Saito,
Takeshi Yamada:
Recommendation method for extending subscription periods.
KDD 2006: 574-579 |
| 43 | EE | 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 | EE | Masahiro Kimura,
Kazumi Saito:
Approximate Solutions for the Influence Maximization Problem in a Social Network.
KES (2) 2006: 937-944 |
| 41 | EE | Kazumi Saito,
Ryohei Nakano:
Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach.
KES (2) 2006: 945-952 |
| 40 | EE | 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 | EE | Masahiro Kimura,
Kazumi Saito:
Tractable Models for Information Diffusion in Social Networks.
PKDD 2006: 259-271 |
| 38 | EE | 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 | EE | Akinori Fujino,
Naonori Ueda,
Kazumi Saito:
A Classifier Design Based on Combining Multiple Components by Maximum Entropy Principle.
AIRS 2005: 423-438 |
| 35 | EE | Yusuke Tanahashi,
Kazumi Saito,
Ryohei Nakano:
Model Selection and Weight Sharing of Multi-layer Perceptrons.
KES (4) 2005: 716-722 |
| 34 | EE | 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 | EE | 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 | EE | 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 | EE | Yusuke Tanahashi,
Kazumi Saito,
Ryohei Nakano:
Piecewise Multivariate Polynomials Using a Four-Layer Perceptron.
KES 2004: 602-608 |
| 30 | EE | Yuji Kaneda,
Naonori Ueda,
Kazumi Saito:
Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization.
KES 2004: 616-623 |
| 29 | EE | Tomoharu Iwata,
Kazumi Saito:
Visualisation of Anomaly Using Mixture Model.
KES 2004: 624-631 |
| 28 | EE | Tomoharu Iwata,
Kazumi Saito,
Naonori Ueda,
Sean Stromsten,
Thomas L. Griffiths,
Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization.
NIPS 2004 |
| 27 | EE | Masahiro Kimura,
Kazumi Saito,
Naonori Ueda:
Modeling of growing networks with directional attachment and communities.
Neural Networks 17(7): 975-988 (2004) |
| 26 | EE | 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 | EE | 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 | EE | Kazumi Saito,
Dileep George,
Stephen D. Bay,
Jeff Shrager:
Inducing Biological Models from Temporal Gene Expression Data.
Discovery Science 2003: 468-469 |
| 23 | EE | 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 | EE | Kazumi Saito,
Ryohei Nakano:
Structuring Neural Networks through Bidirectional Clustering of Weights.
Discovery Science 2002: 206-219 |
| 19 | EE | Kazumi Saito,
Stephen D. Bay,
Pat Langley:
Revising Qualitative Models of Gene Regulation.
Discovery Science 2002: 59-70 |
| 18 | EE | Naonori Ueda,
Kazumi Saito:
Single-shot detection of multiple categories of text using parametric mixture models.
KDD 2002: 626-631 |
| 17 | EE | Naonori Ueda,
Kazumi Saito:
Parametric Mixture Models for Multi-Labeled Text.
NIPS 2002: 721-728 |
| 16 | EE | Ryohei Nakano,
Kazumi Saito:
Discovering Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables.
Progress in Discovery Science 2002: 482-493 |
| 15 | EE | Kazumi Saito,
Pat Langley:
Discovering Empirical Laws of Web Dynamics.
SAINT 2002: 168-175 |
| 14 | EE | Kazumi Saito,
Ryohei Nakano:
Extracting regression rules from neural networks.
Neural Networks 15(10): 1279-1288 (2002) |
| 2001 |
|---|
| 13 | EE | 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 | EE | Ryohei Nakano,
Kazumi Saito:
Finding Polynomials to Fit Multivariate Data Having Numeric and Nominal Variables.
IDA 2001: 258-267 |
| 2000 |
|---|
| 11 | EE | 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 | EE | Ryohei Nakano,
Kazumi Saito:
Discovery of a Set of Nominally Conditioned Polynomials.
Discovery Science 1999: 287-298 |
| 1998 |
|---|
| 7 | EE | 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 | EE | 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 | EE | 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- |
