Seishi Okamoto

发表

Discovery of Areas with Locally Maximal Confidence from Location Data

Hiroya Inakoshi, Hiroaki Morikawa, Tatsuya Asai, 2014, DASFAA.

Chimera: Stream-Oriented XML Filtering/Querying Engine

Masayuki Takeda, Hiroya Inakoshi, Tatsuya Asai, 2010, DASFAA.

An Assistant Interface for Finding Query-Related Proper Nouns

Tomoya Iwakura, Seishi Okamoto, Kanji Uchino, 2007, KES.

A Fast Boosting-based Learner for Feature-Rich Tagging and Chunking

Tomoya Iwakura, Seishi Okamoto, 2008, CoNLL.

Facilitating Search through Visualized Results

Tomoya Iwakura, Takahisa Ando, Seishi Okamoto, 2009, HCI.

An Average Predictive Accuracy of the Nearest Neighbor Classifier

Ken Satoh, Seishi Okamoto, 1994, EWCBR.

An Upgrade Method of Enterprise Search and its Evaluation based on a Real Practice

Tomoya Iwakura, Takahisa Ando, Seishi Okamoto, 2010 .

Training Parse Trees for Efficient VF Coding

Satoshi Yoshida, Takuya Kida, Tatsuya Asai, 2010, SPIRE.

Fast Discovery of Interesting Rules

Nobuhiro Yugami, Seishi Okamoto, Yuiko Ohta, 2000, PAKDD.

Instance Guided Rule Induction

Nobuhiro Yugami, Seishi Okamoto, Yuiko Ohta, 1998, Discovery Science.

An AdaBoost for Efficient Use of Confidences of Weak Hypotheses on Text Categorization

Tomoya Iwakura, Takahiro Saitou, Seishi Okamoto, 2014, PRICAI.

Clustering Algorithm for Large-Scale Databases

Nobuhiro Yugami, Seishi Okamoto, Yuiko Ohta, 1997 .

Generalized Average-Case Analyses of the Nearest Neighbor Algorithm

Nobuhiro Yugami, Seishi Okamoto, 2000, ICML.

Improving Parse Trees for Efficient Variable-to-Fixed Length Codes

Satoshi Yoshida, Takuya Kida, Tatsuya Asai, 2012, J. Inf. Process..

An AdaBoost Using a Weak-Learner Generating Several Weak-Hypotheses for Large Training Data of Natural Language Processing

Kazuo Asakawa, Tomoya Iwakura, Seishi Okamoto, 2010 .

Improving Named Entity Extraction Accuracy using Unlabeled Data and Several Extractors (pp. 29-38)

Tomoya Iwakura, Seishi Okamoto, 2009, Polytech. Open Libr. Int. Bull. Inf. Technol. Sci..

Theoretical Analysis of Case Retrieval Method Based on Neighborhood of a New Problem

Nobuhiro Yugami, Seishi Okamoto, S. Okamoto, 1997, ICCBR.

An Average-Case Analysis of k-Nearest Neighbor Classifier

Seishi Okamoto, Ken Satoh, Ken Satoh, 1995, ICCBR.

An Adaptive Algorithm for Splitting Large Sets of Strings and Its Application to Efficient External Sorting

Hiroki Arimura, Tatsuya Asai, Seishi Okamoto, 2008, PAKDD Workshops.

RSS Feed Generation from Legacy HTML Pages

Jun Wang, Tetsuro Takahashi, Seishi Okamoto, 2006, APWeb.

Prediction of social bookmarking based on a behavior transition model

Mitsuru Ishizuka, Yutaka Matsuo, Seishi Okamoto, 2010, SAC '10.

An Assistant Tool for Concealing Personal Information in Text

Tomoya Iwakura, Seishi Okamoto, 2007, HCI.

Effects of domain characteristics on instance-based learning algorithms

Nobuhiro Yugami, Seishi Okamoto, S. Okamoto, 2003, Theor. Comput. Sci..

Theoretical Analysis of the Nearest Neighbor Classifier in Noisy Domains

Nobuhiro Yugami, Seishi Okamoto, 1996, ICML.

An Average-Case Analysis of the k-Nearest Neighbar Classifier for Noisy Domains

Nobuhiro Yugami, Seishi Okamoto, S. Okamoto, 1997, IJCAI.

Personalization System based on Dynamic Learning

Hiroya Inakoshi, Seishi Okamoto, Akira Sato, .