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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2011)
Search Results: Keywords 'from:2012-03-12 to:2012-03-12'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2012-03-12 10:00 |
Tokyo | The Institute of Statistical Mathematics | Chance Adjusted Infinite Relational Model for Asymmetric and Individually Different Relational Data Analysis Iku Ohama, Hiromi Iida (Panasonic), Takuya Kida, Hiroki Arimura (Hokkaido Univ.)  IBISML2011-87 |
We propose a new generative model which analyses asymmetric and individually different relational data. In our proposed ... [more] | IBISML2011-87 pp.1-8 |
| IBISML | 2012-03-12 10:25 |
Tokyo | The Institute of Statistical Mathematics | Bayesian Network Structure Estimation based on the Bayesian/MDL Criteria when both Discrete and Continuous Variables are Present Joe Suzuki (Osaka Univ.) IBISML2011-88 |
We consider estimation of Bayesian network structures given a finite number of examples when both discrete and continuou... [more] | IBISML2011-88 pp.9-14 |
| IBISML | 2012-03-12 10:50 |
Tokyo | The Institute of Statistical Mathematics | Detecting Latent Structural Changes via Latent Dirichlet Allocation Masashi Ueda, Ryota Tomioka, Kenji Yamanishi (The Univ. of Tokyo), Katsuhiko Ishiguro, Hiroshi Sawada, Naonori Ueda (NTT) IBISML2011-89 |
Detecting changes in consumers' latent preference is a fundamental challenge for improving recommendation systems as wel... [more] | IBISML2011-89 pp.15-20 |
| IBISML | 2012-03-12 11:25 |
Tokyo | The Institute of Statistical Mathematics | Fully Bayesian speaker clustering based on hierarchical structured Dirichlet process mixture model Naohiro Tawara, Tetsuji Ogawa (Waseda Univ.), Shinji Watanabe (NTT/MERL), Atsushi Nakamura (NTT), Tetsunori Kobayashi (Waseda Univ.) IBISML2011-90 |
We proposed a novel speaker clustering method by estimating the structure of a fully Bayesian utterance generative model... [more] | IBISML2011-90 pp.21-28 |
| IBISML | 2012-03-12 11:50 |
Tokyo | The Institute of Statistical Mathematics | Hyperparameter Selection of Infinite Gaussian Mixture Model via Widely Applicable Information Criterion Takushi Miki, Masahiro Kohjima, Sumio Watanabe (titech) IBISML2011-91 |
Recently, nonparametric Bayesian method is applied to wide range of research fields such as natural language processing,... [more] | IBISML2011-91 pp.29-33 |
| IBISML | 2012-03-12 13:30 |
Tokyo | The Institute of Statistical Mathematics | [Invited Talk]
Subsequence data mining revisited Shin Ando (Gunma Univ.) |
[more] | |
| IBISML | 2012-03-12 14:40 |
Tokyo | The Institute of Statistical Mathematics | Kernel Bellman Equations in POMDPs Yu Nishiyama (ISM), Abdeslam Boularias (MPI), Arthur Gretton (UCL), Kenji Fukumizu (ISM) IBISML2011-92 |
We propose to handle POMDPs in reproducing kernel Hilbert spaces (RKHSs) using recent kernel methods of embedding distri... [more] | IBISML2011-92 pp.35-42 |
| IBISML | 2012-03-12 15:05 |
Tokyo | The Institute of Statistical Mathematics | Model Selection of Indirect Value Function Estimation Masahiro Kohjima (Tokyo Tech) IBISML2011-93 |
Reinforcement learning is a method to obtain a policy which maximizes expected return and is applied to wide range of re... [more] | IBISML2011-93 pp.43-48 |
| IBISML | 2012-03-12 15:30 |
Tokyo | The Institute of Statistical Mathematics | Apprenticeship Learning for Model Parameters of Partially Observable Environments Takaki Makino (Univ. of Tokyo), Johane Takeuchi (HRI-JP) IBISML2011-94 |
We consider apprentice learning, i.e., to make an agent learn a task by observing an expert demonstrating the task, in a... [more] | IBISML2011-94 pp.49-54 |
| IBISML | 2012-03-12 15:55 |
Tokyo | The Institute of Statistical Mathematics | Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration Tingting Zhao, Hirotaka Hachiya, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-95 |
[more] | IBISML2011-95 pp.55-62 |
| IBISML | 2012-03-12 16:30 |
Tokyo | The Institute of Statistical Mathematics | Canonical Multiple Sequence Alignment for High-dimensional Multiple Time-series Analysis Takamitsu Matsubara (NAIST/ATR), Jun Morimoto (ATR) IBISML2011-96 |
Temporal alignment of time series is a fundamental problem as the first step for many purposes. Especially, the techniqu... [more] | IBISML2011-96 pp.63-68 |
| IBISML | 2012-03-12 16:55 |
Tokyo | The Institute of Statistical Mathematics | Heterogeneous Time Series Clustering based on Infinite-Length Model Distance Shunsuke Hirose, Katsuyuki Izumi (SAS) IBISML2011-97 |
This paper addresses the issue of heterogeneous time series clustering, which means clustering of time series having var... [more] | IBISML2011-97 pp.69-76 |
| IBISML | 2012-03-12 17:20 |
Tokyo | The Institute of Statistical Mathematics | Detecting Long-term Trending Topics in Social Networks Shota Saito, Ryota Tomioka, Kenji Yamanishi (The Univ. of Tokyo) IBISML2011-98 |
In social networking services (SNSs), long-term trending topics are extremely rare and valuable. In this paper, we propo... [more] | IBISML2011-98 pp.77-84 |
| IBISML | 2012-03-13 10:00 |
Tokyo | The Institute of Statistical Mathematics | On d-consistency for high-dimensional discrimination analysis Takanori Ayano (Osaka Univ.) IBISML2011-99 |
Recently, in many fields such as microarray analysis, we need to analyze high-dimensional data with small sample sizes. ... [more] | IBISML2011-99 pp.85-88 |
| IBISML | 2012-03-13 10:25 |
Tokyo | The Institute of Statistical Mathematics | An Accuracy Analysis of Bayes Latent Variable Estimation with Redundant Variables Keisuke Yamazaki (Tokyo Inst. of Tech.) IBISML2011-100 |
(To be available after the conference date) [more] | IBISML2011-100 pp.89-95 |
| IBISML | 2012-03-13 11:00 |
Tokyo | The Institute of Statistical Mathematics | A Study on Decision Boundary Stability in Active Learning with Support Vector Machine Takeru Takahashi, Yuji Waizumi, Kazuo Hashimoto (Tohoku Univ.) IBISML2011-101 |
Studies on active learning have advanced in order to improve the performance of supervised learning machines efficientl... [more] |
IBISML2011-101 pp.97-101 |
| IBISML | 2012-03-13 11:25 |
Tokyo | The Institute of Statistical Mathematics | Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching Marthinus Christoffel du Plessis, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-102 |
[more] | IBISML2011-102 pp.103-108 |
| IBISML | 2012-03-13 13:30 |
Tokyo | The Institute of Statistical Mathematics | [Invited Talk]
Asymptotic estimation theory and Information geometry via computational algebra Kei Kobayashi (ISM) |
[more] | |
| IBISML | 2012-03-13 14:40 |
Tokyo | The Institute of Statistical Mathematics | Matrix and Tensor Factorization with Aggregated Observations Yoshifumi Aimoto, Hisashi Kashima (Univ. of Tokyo) IBISML2011-103 |
Matrix and tensor factorization with low-rank assumption are fundamental tools in data analysis. However, the existing m... [more] | IBISML2011-103 pp.109-116 |
| IBISML | 2012-03-13 15:05 |
Tokyo | The Institute of Statistical Mathematics | A Method for Packet Loss Rate Estimation Using Active and Passive Measurements Atsushi Miyamoto, Kazuho Watanabe, Kazushi Ikeda (NAIST) IBISML2011-104 |
It is an important problem for a network manager to understand characteristics of a network. A method for estimating an ... [more] | IBISML2011-104 pp.117-121 |
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