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2020 – today
- 2022
[c45]Rina Obata, Kei Ohnishi, Makoto Fukumoto
, Miho Ohsaki:
Experiment to Investigate Awareness of Tastes for Users in Interactive Evolutionary Computation. SCIS/ISIS 2022: 1-6- [c44]Ginji Hayashi, Shigeru Katagiri, Xugang Lu, Miho Ohsaki:
An Investigation of Feature Difference Between Child and Adult Voices Using Line Spectral Pairs. SPML 2022: 94-100 - [c43]Koki Kishishita, Shigeru Katagiri, Miho Ohsaki:
A Proposal of an Improved Maximum Bayes Boundary-Ness Training Method. SPML 2022: 246-254 - 2021
- [j12]Miho Ohsaki, Naoya Kishimoto, Hayato Sasaki, Ryoji Ikeura, Shigeru Katagiri, Kei Ohnishi, Yakub Sebastian, Patrick H. H. Then:
NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso. IEEE Access 9: 132033-132052 (2021) - [j11]David Ha, Shigeru Katagiri, Hideyuki Watanabe, Miho Ohsaki:
An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation. J. Signal Process. Syst. 93(9): 1057-1084 (2021) - [c42]Miho Ohsaki, Hayato Sasaki, Naoya Kishimoto, Shigeru Katagiri, Kei Ohnishi, Yakub Sebastian, Patrick H. H. Then:
Evaluation of the Neural-network-based Method to Discover Sets and Representatives of Nonlinearly Dependent Variables. CYBCONF 2021: 101-106 - 2020
- [j10]David Ha, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection. J. Signal Process. Syst. 92(2): 135-151 (2020) - [c41]Masaya Kato, Miho Ohsaki, Kei Ohnishi:
Genetic Algorithms Using Neural Network Regression and Group Lasso for Dynamic Selection of Crossover Operators. SCIS/ISIS 2020: 1-6
2010 – 2019
- 2019
- [c40]Yuya Tomotoshi, David Ha, Emilie Delattre, Hideyuki Watanabe, Xugang Lu, Shigeru Katagiri, Miho Ohsaki:
Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers. IUKM 2019: 295-307 - [c39]Masahiro Senda, David Ha, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Maximum Bayes Boundary-Ness Training For Pattern Classification. SPML 2019: 18-28 - [c38]Naoto Umezaki, Takumi Okubo, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition. SPML 2019: 62-72 - 2018
- [c37]Miho Ohsaki, Hayato Sasaki, Naoya Kishimoto, Shigeru Katagiri, Patrick Hang Hui Then:
Discovery of Sets and Representatives of Variables in Co-nonlinear Relationships by Neural Network Regression and Group Lasso. BIBM 2018: 2287-2294 - [c36]David Ha, Emilie Delattre, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Optimal Classifier Model Status Selection using Bayes boundary uncertainty. MLSP 2018: 1-6 - [c35]Miho Ohsaki, Hayato Sasaki, Hiroharu Kawanaka, Shigeru Katagiri:
Body Part Diagram Recognition in Medical Records: Application of the Histograms of Oriented Gradients and the Mahalanobis-Distance-Based Classifier. SCIS&ISIS 2018: 1242-1247 - 2017
- [j9]Miho Ohsaki, Peng Wang, Kenji Matsuda, Shigeru Katagiri, Hideyuki Watanabe, Anca L. Ralescu:
Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification. IEEE Trans. Knowl. Data Eng. 29(9): 1806-1819 (2017) - [c34]Ryoma Tani, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Compact kernel classifiers trained with minimum classification error criterion. MLSP 2017: 1-6 - 2015
- [c33]Miho Ohsaki, Kenji Matsuda, Peng Wang, Shigeru Katagiri, Hideyuki Watanabe:
Formulation of the kernel logistic regression based on the confusion matrix. CEC 2015: 2327-2334 - 2014
- [j8]Hideyuki Watanabe, Tsukasa Ohashi, Shigeru Katagiri, Miho Ohsaki, Shigeki Matsuda, Hideki Kashioka:
Robust and Efficient Pattern Classification using Large Geometric Margin Minimum Classification Error Training. J. Signal Process. Syst. 74(3): 297-310 (2014) - [j7]Hideyuki Watanabe, Jun'ichi Tokuno, Tsukasa Ohashi, Shigeru Katagiri, Miho Ohsaki, Shigeki Matsuda, Hideki Kashioka:
Minimum Classification Error Training Incorporating Automatic Loss Smoothness Determination. J. Signal Process. Syst. 74(3): 311-322 (2014) - 2012
- [c32]Tsukasa Ohashi, Hideyuki Watanabe, Jun'ichi Tokuno, Shigeru Katagiri, Miho Ohsaki, Shigeki Matsuda, Hideki Kashioka:
Increasing virtual samples through loss smoothness determination in large geometric margin minimum classification error training. ICASSP 2012: 2081-2084 - [c31]Kenji Matsuda, Miho Ohsaki, Shigeru Katagiri, Hideto Yokoi, Katsuhiko Takabayashi:
Application of kernel logistic regression to the prediction of liver fibrosis stages in chronic hepatitis C. SCIS&ISIS 2012: 780-784 - 2011
- [c30]Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Minimum classification error training with geometric margin enhancement for robust pattern recognition. MLSP 2011: 1-6 - [c29]Hideyuki Watanabe, Jun'ichi Tokuno, Tsukasa Ohashi, Shigeru Katagiri, Miho Ohsaki:
Minimum classification error training with automatic setting of loss smoothness. MLSP 2011: 1-6 - 2010
- [j6]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Improving a rule evaluation support method based on objective indices. Int. J. Adv. Intell. Paradigms 2(2/3): 180-197 (2010) - [c28]Hideyuki Watanabe, Shigeru Katagiri, Kouta Yamada, Erik McDermott, Atsushi Nakamura, Shinji Watanabe, Miho Ohsaki:
Minimum Error Classification with geometric margin control. ICASSP 2010: 2170-2173
2000 – 2009
- 2009
- [c27]Miho Ohsaki:
Improvement of interactive EC fitting based on substitute evaluation using sound volume preference. FUZZ-IEEE 2009: 1597 - [c26]Miho Ohsaki, Masakazu Nakase, Shigeru Katagiri:
Analysis of Subsequence Time-Series Clustering Based on Moving Average. ICDM 2009: 902-907 - [p2]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method. Mining Complex Data 2009: 95-111 - 2008
- [c25]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Finding Functional Groups of Objective Rule Evaluation Indices Using PCA. PAKM 2008: 197-206 - [p1]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms to Support Human Rule Evaluation with Predicting Interestingness Based on Objective Rule Evaluation Indices. Communications and Discoveries from Multidisciplinary Data 2008: 269-282 - 2007
- [j5]Miho Ohsaki, Hidenao Abe, Shusaku Tsumoto, Hideto Yokoi, Takahira Yamaguchi:
Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artif. Intell. Medicine 41(3): 177-196 (2007) - [j4]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms to Support Human Rule - Evaluation Based on Objective Rule Evaluation Indices. Data Sci. J. 6: 285-296 (2007) - [j3]Miho Ohsaki, Hidenao Abe, Takahira Yamaguchi:
Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification. New Gener. Comput. 25(3): 213-222 (2007) - [j2]Hideyuki Takagi, Miho Ohsaki:
Interactive Evolutionary Computation-Based Hearing Aid Fitting. IEEE Trans. Evol. Comput. 11(3): 414-427 (2007) - [c24]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms to Construct Rule Evaluation Models Based on Objective Rule Evaluation Indices. IEEE ICCI 2007: 212-221 - [c23]Hidenao Abe, Satoru Hirabayashi, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Accuracies of a Trading Rule Mining Method Based on Temporal Pattern Extraction. MCD 2007: 72-81 - [c22]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira Yamaguchi:
Evaluation of Learning Costs of Rule Evaluation Models Based on Objective Indices to Predict Human Hypothesis Construction Phases. GrC 2007: 458-464 - [c21]Hidenao Abe, Hideto Yokoi, Miho Ohsaki, Takahira Yamaguchi:
Developing an Integrated Time-Series Data Mining Environment for Medical Data Mining. ICDM Workshops 2007: 127-132 - [c20]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating a Constructive Meta-learning Algorithm for a Rule Evaluation Support Method Based on Objective Indices. KES (2) 2007: 934-941 - [c19]Hidenao Abe, Hideto Yokoi, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Models with Transitions of Human Interests Based on Objective Rule Evaluation Indices. MedInfo 2007: 581-585 - [c18]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating learning algorithms for a rule evaluation support method. SMC 2007: 3784-3789 - 2006
- [c17]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms Composed by a Constructive Meta-Learning Scheme for a Rule Evaluation Support Method. ICDM Workshops 2006: 305-310 - [c16]Miho Ohsaki, Hidenao Abe, Shusaku Tsumoto, Hideto Yokoi, Takahira Yamaguchi:
Proposal of Medical KDD Support User Interface Utilizing Rule Interestingness Measures. ICDM Workshops 2006: 759-764 - [c15]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Algorithms for a Rule Evaluation Support Method Based on Objective Rule Evaluation Indices. ISMIS 2006: 379-388 - [c14]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Model Construction Methods with Objective Rule Evaluation Indices to Support Human Experts. MDAI 2006: 93-104 - [c13]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating a Rule Evaluation Support Method Based on Objective Rule Evaluation Indices. PAKDD 2006: 509-519 - [c12]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Hideto Yokoi, Takahira Yamaguchi:
Evaluating Learning Algorithms with Meta-learning Schemes for a Rule Evaluation Support Method Based on Objective Indices. PKAW 2006: 75-88 - [c11]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Evaluating Learning Models for a Rule Evaluation Support Method Based on Objective Indices. RSCTC 2006: 687-695 - [c10]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
Developing a Rule Evaluation Support Method Based on Objective Indices. RSKT 2006: 456-461 - 2005
- [c9]Hidenao Abe, Miho Ohsaki, Shusaku Tsumoto, Takahira Yamaguchi:
Evaluating a Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indices - A Case Study with a Meningitis Data Mining Result. HIS 2005: 169-174 - [c8]Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi:
A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes. ICDM 2005: 549-552 - [c7]Hidenao Abe, Miho Ohsaki, Hideto Yokoi, Takahira Yamaguchi:
Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis. JSAI Workshops 2005: 425-435 - 2004
- [c6]Miho Ohsaki, Yoshinori Sato, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi:
Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining. IEA/AIE 2004: 1072-1081 - [c5]Miho Ohsaki, Shinya Kitaguchi, Kazuya Okamoto, Hideto Yokoi, Takahira Yamaguchi:
Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis. PKDD 2004: 362-373 - 2003
- [c4]Miho Ohsaki, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi:
Investigation of Rule Interestingness in Medical Data Mining. Active Mining 2003: 174-189 - [c3]Miho Ohsaki, Yoshifumi Banno, Tomohiro Yoshikawa, Tsuyoshi Shinogi, Nobuharu Tsuruoka:
Sampling point extraction based on genetic algorithm and function approximation of a search space. CIRA 2003: 300-305 - 2000
- [c2]Miho Ohsaki, Takahiro Sugiyama, Hideaki Ohno:
Evaluation of edge detection methods through psychological tests-is the detected edge really desirable for humans? SMC 2000: 671-677
1990 – 1999
- 1998
- [j1]Miho Ohsaki, Hideyuki Takagi, Kimiko Ohya:
An input method using discrete fitness values for interactive GA. J. Intell. Fuzzy Syst. 6(1): 131-145 (1998) - [c1]Miho Ohsaki, Hideyuki Takagi:
Improvement of presenting interface by predicting the evaluation order to reduce the burden of human interactive EC operators. SMC 1998: 1284-1289
Coauthor Index
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