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Nobuo Suematsu
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2010 – 2019
- 2019
- [j6]Kazuya Ose, Kazunori Iwata
, Nobuo Suematsu:
Sampling Shape Contours Using Optimization over a Geometric Graph. IEICE Trans. Inf. Syst. 102-D(12): 2547-2556 (2019) - 2018
- [c18]Minori Ihara, Kazunori Iwata, Nobuo Suematsu, Kazushi Mimura:
Typical Performance of Sparse Signal Recovery from a Linear Measurement with Large Coherence. ISITA 2018: 427-431 - [c17]Hiroki Yamamoto, Kazunori Iwata, Nobuo Suematsu, Kazushi Mimura:
A Shape Matching Method Considering Computational Feasibility. SPML 2018: 115-119 - 2017
- [c16]Tsukasa Okamoto, Kazunori Iwata, Nobuo Suematsu:
Extending the Full Procrustes Distance to Anisotropic Scale in Shape Analysis. ACPR 2017: 634-639 - [c15]Kazuya Ose, Kazunori Iwata, Nobuo Suematsu:
A sampling method for processing contours drawn with an uncertain stroke order and number. MVA 2017: 468-471 - 2015
- [j5]Kosei Kurisu, Nobuo Suematsu, Kazunori Iwata
, Akira Hayashi:
A Spatially Correlated Mixture Model for Image Segmentation. IEICE Trans. Inf. Syst. 98-D(4): 930-937 (2015) - [c14]Kazunori Iwata, Nobuo Suematsu, Akira Hayashi:
A new shape descriptor based on an angular-linear probability distribution. ACPR 2015: 484-488 - 2013
- [j4]Akira Hayashi, Kazunori Iwata
, Nobuo Suematsu:
Marginalized Viterbi algorithm for hierarchical hidden Markov models. Pattern Recognit. 46(12): 3452-3459 (2013) - [c13]Kosei Kurisu, Nobuo Suematsu, Kazunori Iwata, Akira Hayashi:
Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors. ACPR 2013: 59-63 - [c12]Akira Hayashi, Kazunori Iwata, Nobuo Suematsu:
Finding the Most Likely Upper Level State Sequence for Hierarchical HMMs. SLSP 2013: 111-122 - 2012
- [c11]Nobuo Suematsu, Akira Hayashi:
Time series alignment with Gaussian processes. ICPR 2012: 2355-2358 - 2011
- [j3]Katsutoshi Ueaoki, Kazunori Iwata
, Nobuo Suematsu, Akira Hayashi:
Matching Handwritten Line Drawings with Von Mises Distributions. IEICE Trans. Inf. Syst. 94-D(12): 2487-2494 (2011) - [c10]Satoshi Kaneko, Akira Hayashi, Nobuo Suematsu, Kazunori Iwata:
Hierarchical Hidden Conditional Random Fields for Information Extraction. LION 2011: 191-202 - 2010
- [c9]Shinji Akimoto, Nobuo Suematsu:
A nonparametric Bayesian approach to time series alignment. NaBIC 2010: 648-653
2000 – 2009
- 2008
- [j2]Kumiko Maebashi, Nobuo Suematsu, Akira Hayashi:
Component Reduction for Gaussian Mixture Models. IEICE Trans. Inf. Syst. 91-D(12): 2846-2853 (2008) - 2007
- [c8]Kumiko Maebashi, Nobuo Suematsu, Akira Hayashi:
Component Reduction for Hierarchical Mixture Model Construction. ICONIP (2) 2007: 326-335 - 2006
- [j1]Yuko Mizuhara, Akira Hayashi, Nobuo Suematsu:
Embedding of time series data by using dynamic time warping distances. Syst. Comput. Jpn. 37(3): 1-9 (2006) - [c7]Mitsuharu Emoto, Akira Hayashi, Nobuo Suematsu, Kazunori Iwata:
View Independent Gait Identification Using a Particle Filter. AVSS 2006: 74 - 2005
- [c6]Naoto Gotou, Akira Hayashi, Nobuo Suematsu:
Learning with Segment Boundaries for Hierarchical HMMs. ICAPR (1) 2005: 538-543 - [c5]Akira Hayashi, Yuko Mizuhara, Nobuo Suematsu:
Embedding Time Series Data for Classification. MLDM 2005: 356-365 - 2002
- [c4]Nobuo Suematsu, Akira Hayashi:
A multiagent reinforcement learning algorithm using extended optimal response. AAMAS 2002: 370-377
1990 – 1999
- 1998
- [c3]Akira Hayashi, Nobuo Suematsu:
Viewing Classifier Systems as Model Free Learning in POMDPs. NIPS 1998: 989-995 - [c2]Nobuo Suematsu, Akira Hayashi:
A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory. NIPS 1998: 1059-1065 - 1997
- [c1]Nobuo Suematsu, Akira Hayashi, Shigang Li:
A Bayesian Approach to Model Learning in Non-Markovian Environments. ICML 1997: 349-357
Coauthor Index
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