Nobuo Suematsu - researchr alias

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Viewing Publication 1 - 24 from 24

2019

Sampling Shape Contours Using Optimization over a Geometric GraphKazuya Ose, Kazunori Iwata 0002, Nobuo Suematsu. ieicet, 102-D(12):2547-2556, 2019. [doi]

2018

Typical Performance of Sparse Signal Recovery from a Linear Measurement with Large CoherenceMinori Ihara, Kazunori Iwata, Nobuo Suematsu, Kazushi Mimura. isita 2018: 427-431 [doi]

A Shape Matching Method Considering Computational FeasibilityHiroki Yamamoto, Kazunori Iwata 0001, Nobuo Suematsu, Kazushi Mimura. spml 2018: 115-119 [doi]

2017

Extending the Full Procrustes Distance to Anisotropic Scale in Shape AnalysisTsukasa Okamoto, Kazunori Iwata, Nobuo Suematsu. acpr 2017: 634-639 [doi]

A sampling method for processing contours drawn with an uncertain stroke order and numberKazuya Ose, Kazunori Iwata, Nobuo Suematsu. mva 2017: 468-471 [doi]

2015

A new shape descriptor based on an angular-linear probability distributionKazunori Iwata, Nobuo Suematsu, Akira Hayashi. acpr 2015: 484-488 [doi]

A Spatially Correlated Mixture Model for Image SegmentationKosei Kurisu, Nobuo Suematsu, Kazunori Iwata, Akira Hayashi. ieicet, 98-D(4):930-937, 2015. [doi]

2013

Marginalized Viterbi algorithm for hierarchical hidden Markov modelsAkira Hayashi, Kazunori Iwata, Nobuo Suematsu. PR, 46(12):3452-3459, 2013. [doi]

Finding the Most Likely Upper Level State Sequence for Hierarchical HMMsAkira Hayashi, Kazunori Iwata, Nobuo Suematsu. slsp 2013: 111-122 [doi]

Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process PriorsKosei Kurisu, Nobuo Suematsu, Kazunori Iwata, Akira Hayashi. acpr 2013: 59-63 [doi]

2012

Time series alignment with Gaussian processesNobuo Suematsu, Akira Hayashi. icpr 2012: 2355-2358 [doi]

2011

Hierarchical Hidden Conditional Random Fields for Information ExtractionSatoshi Kaneko, Akira Hayashi, Nobuo Suematsu, Kazunori Iwata. lion 2011: 191-202 [doi]

Matching Handwritten Line Drawings with Von Mises DistributionsKatsutoshi Ueaoki, Kazunori Iwata, Nobuo Suematsu, Akira Hayashi. ieicet, 94-D(12):2487-2494, 2011. [doi]

2010

A nonparametric Bayesian approach to time series alignmentShinji Akimoto, Nobuo Suematsu. nabic 2010: 648-653 [doi]

2008

Component Reduction for Gaussian Mixture ModelsKumiko Maebashi, Nobuo Suematsu, Akira Hayashi. ieicet, 91-D(12):2846-2853, 2008. [doi]

2007

Component Reduction for Hierarchical Mixture Model ConstructionKumiko Maebashi, Nobuo Suematsu, Akira Hayashi. iconip 2008: 326-335 [doi]

2006

View Independent Gait Identification Using a Particle FilterMitsuharu Emoto, Akira Hayashi, Nobuo Suematsu, Kazunori Iwata. avss 2006: 74 [doi]

Embedding of time series data by using dynamic time warping distancesYuko Mizuhara, Akira Hayashi, Nobuo Suematsu. scjapan, 37(3):1-9, 2006. [doi]

2005

Learning with Segment Boundaries for Hierarchical HMMsNaoto Gotou, Akira Hayashi, Nobuo Suematsu. icapr 2005: 538-543 [doi]

Embedding Time Series Data for ClassificationAkira Hayashi, Yuko Mizuhara, Nobuo Suematsu. mldm 2005: 356-365 [doi]

2002

A multiagent reinforcement learning algorithm using extended optimal responseNobuo Suematsu, Akira Hayashi. atal 2002: 370-377 [doi]

1998

Viewing Classifier Systems as Model Free Learning in POMDPsAkira Hayashi, Nobuo Suematsu. nips 1999: 989-995 [doi]

A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term MemoryNobuo Suematsu, Akira Hayashi. nips 1999: 1059-1065 [doi]

1997

A Bayesian Approach to Model Learning in Non-Markovian EnvironmentsNobuo Suematsu, Akira Hayashi, Shigang Li. icml 1997: 349-357

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