A Bayesian Approach to Model Learning in Non-Markovian Environments. - Nobuo Suematsu; Akira Hayashi; Shigang Li 001 (ICML, 1997)

Viewing Classifier Systems as Model Free Learning in POMDPs. - Akira Hayashi; Nobuo Suematsu (NIPS, 1998)

A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory. - Nobuo Suematsu; Akira Hayashi (NIPS, 1998)

A multiagent reinforcement learning algorithm using extended optimal response. - Nobuo Suematsu; Akira Hayashi (AAMAS, 2002)

Learning with Segment Boundaries for Hierarchical HMMs. - Naoto Gotou; Akira Hayashi; Nobuo Suematsu (ICAPR (1), 2005)

Embedding Time Series Data for Classification. - Akira Hayashi; Yuko Mizuhara; Nobuo Suematsu (MLDM, 2005)

View Independent Gait Identification Using a Particle Filter. - Mitsuharu Emoto; Akira Hayashi; Nobuo Suematsu; Kazunori Iwata 001 (AVSS, 2006)

Embedding of time series data by using dynamic time warping distances. - Yuko Mizuhara; Akira Hayashi; Nobuo Suematsu (Syst. Comput. Jpn., 2006)

Component Reduction for Hierarchical Mixture Model Construction. - Kumiko Maebashi; Nobuo Suematsu; Akira Hayashi (ICONIP (2), 2007)

Component Reduction for Gaussian Mixture Models. - Kumiko Maebashi; Nobuo Suematsu; Akira Hayashi (IEICE Trans. Inf. Syst., 2008)

A nonparametric Bayesian approach to time series alignment. - Shinji Akimoto; Nobuo Suematsu (NaBIC, 2010)

Hierarchical Hidden Conditional Random Fields for Information Extraction. - Satoshi Kaneko; Akira Hayashi; Nobuo Suematsu; Kazunori Iwata 001 (LION, 2011)

Matching Handwritten Line Drawings with Von Mises Distributions. - Katsutoshi Ueaoki; Kazunori Iwata 002; Nobuo Suematsu; Akira Hayashi (IEICE Trans. Inf. Syst., 2011)

Time series alignment with Gaussian processes. - Nobuo Suematsu; Akira Hayashi (ICPR, 2012)

Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors. - Kosei Kurisu; Nobuo Suematsu; Kazunori Iwata 001; Akira Hayashi (ACPR, 2013)

Finding the Most Likely Upper Level State Sequence for Hierarchical HMMs. - Akira Hayashi; Kazunori Iwata 001; Nobuo Suematsu (SLSP, 2013)

Marginalized Viterbi algorithm for hierarchical hidden Markov models. - Akira Hayashi; Kazunori Iwata 002; Nobuo Suematsu (Pattern Recognit., 2013)

A new shape descriptor based on an angular-linear probability distribution. - Kazunori Iwata 001; Nobuo Suematsu; Akira Hayashi (ACPR, 2015)

A Spatially Correlated Mixture Model for Image Segmentation. - Kosei Kurisu; Nobuo Suematsu; Kazunori Iwata 002; Akira Hayashi (IEICE Trans. Inf. Syst., 2015)

Extending the Full Procrustes Distance to Anisotropic Scale in Shape Analysis. - Tsukasa Okamoto; Kazunori Iwata 001; Nobuo Suematsu (ACPR, 2017)

A sampling method for processing contours drawn with an uncertain stroke order and number. - Kazuya Ose; Kazunori Iwata 001; Nobuo Suematsu (MVA, 2017)

Typical Performance of Sparse Signal Recovery from a Linear Measurement with Large Coherence. - Minori Ihara; Kazunori Iwata 001; Nobuo Suematsu; Kazushi Mimura (ISITA, 2018)

A Shape Matching Method Considering Computational Feasibility. - Hiroki Yamamoto; Kazunori Iwata 001; Nobuo Suematsu; Kazushi Mimura (SPML, 2018)

Sampling Shape Contours Using Optimization over a Geometric Graph. - Kazuya Ose; Kazunori Iwata 002; Nobuo Suematsu (IEICE Trans. Inf. Syst., 2019)