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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2013)
Search Results: Keywords 'from:2014-03-06 to:2014-03-06'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2014-03-06 13:00 |
Nara | Nara Women's University | A Generic Framework for Forward and Forward-Backward Algorithms on Various Kinds of Data Structures in Machine Learning Ai Azuma, Yuji Matsumoto (NAIST)  IBISML2013-67 |
[more] | IBISML2013-67 pp.1-8 |
| IBISML | 2014-03-06 13:25 |
Nara | Nara Women's University | Simultaneous prediction of multiple physical properties using multi-task learning Tomoaki Iwase (Univ. of Tokyo), Atsuto Seko (Kyoto Univ.), Hisashi Kashima (Univ. of Tokyo) IBISML2013-68 |
We apply several existing techniques and a new model of multi-task learning to the problem of predicting multiple physic... [more] | IBISML2013-68 pp.9-13 |
| IBISML | 2014-03-06 13:50 |
Nara | Nara Women's University | Finding scale-free networks of Gaussian graphical models by sampling Shota Shikita, Osamu Maruyama (Kyushu Univ.) IBISML2013-69 |
The problem of learning the structure of a Gaussian graphical model is to infer the graph representing the relationship ... [more] | IBISML2013-69 pp.15-22 |
| IBISML | 2014-03-06 14:30 |
Nara | Nara Women's University | Study about Over-Learning Phenomenon of Sample Mahalanobis Distances Yasuyuki Kobayashi (Teikyo Univ.) IBISML2013-70 |
When the learning sample size n is near the dimensionality p, the discrepant phenomenon between the distribution of the ... [more] | IBISML2013-70 pp.23-30 |
| IBISML | 2014-03-06 14:55 |
Nara | Nara Women's University | Consideration of Correlation between Users' Evaluating Values and Their Dropouts in Missing Value Prediction Kenta Nishimura, Toshiyuki Tanaka (Kyoto Univ.) IBISML2013-71 |
In user-item relational data, there are sometimes correlations between values and their dropouts. Existing methods under... [more] | IBISML2013-71 pp.31-38 |
| IBISML | 2014-03-06 15:20 |
Nara | Nara Women's University | Focused Tensor Completion
-- Transfer learning for completing a certain slice of a third-order tensor Data -- Taketo Akama, Yukino Baba, Hisashi Kashima (Univ. of Tokyo) IBISML2013-72 |
[more] | IBISML2013-72 pp.39-46 |
| IBISML | 2014-03-07 09:40 |
Nara | Nara Women's University | A Label Completion Approach to Crowd Approximation Toshihiro Watanabe, Hisashi Kashima (Univ. of Tokyo) IBISML2013-73 |
Majority vote is one of the most common methods for crowdsourced label aggregation to get higher-quality labels, but it ... [more] | IBISML2013-73 pp.47-53 |
| IBISML | 2014-03-07 10:05 |
Nara | Nara Women's University | Online Prediction with Bradley-Terry Models and Logistic Models Issei Matsumoto, Kohei Hatano, Eiji Takimoto (Kyushu Univ) IBISML2013-74 |
We consider an online density estimation problem under the Bradley-Terry model which determines the probability of the o... [more] | IBISML2013-74 pp.55-62 |
| IBISML | 2014-03-07 10:30 |
Nara | Nara Women's University | Online Matrix Prediction with Log-Determinant Regularizer Kenichiro Moridomi, Kohei Hatano, Eiji Takimoto (Kyushu Univ.), Koji Tsuda (AIST) IBISML2013-75 |
We consider an online symmetric positive semi-definite matrix prediction problem with convex loss function and Frobenius... [more] | IBISML2013-75 pp.63-70 |
| IBISML | 2014-03-07 11:10 |
Nara | Nara Women's University | Blind Separation of Sparse and Smooth Signals via Approximate Message Passing Algorithm Shigeki Yokoyama, Toshiyuki Tanaka (Kyoto Univ.) IBISML2013-76 |
We consider the problem to recover source signals from noisy mixed ones. This can be described as a matrix reconstructio... [more] | IBISML2013-76 pp.71-78 |
| IBISML | 2014-03-07 11:35 |
Nara | Nara Women's University | Bayesian Test of Independence Takanori Ayano, Joe Suzuki (Osaka Univ.) IBISML2013-77 |
This paper proposes Bayesian estimators of mutual information and independence tests. Given independently emitted $n$ ... [more] |
IBISML2013-77 pp.79-86 |
| IBISML | 2014-03-07 13:30 |
Nara | Nara Women's University | [Invited Talk]
A Brief Introduction to Machine Learning with Submodular Functions and Recent Trends Yoshinobu Kawahara (Osaka Univ.) IBISML2013-78 |
Submodularity is known to be an analogous concept for a set function of convexity in a continuous function. Recently, it... [more] | IBISML2013-78 pp.87-88 |
| IBISML | 2014-03-07 14:45 |
Nara | Nara Women's University | Estimation of Item Preference Parameter with User Review Data Shunichi Mochizuki, Yu Fujimoto, Noboru Murata (Waseda Univ.) IBISML2013-79 |
There are services that recommend items such as shops or products to users based on scores or ratings that other users h... [more] | IBISML2013-79 pp.89-94 |
| IBISML | 2014-03-07 15:10 |
Nara | Nara Women's University | Effective interest point detection and clustering method for action recognition Kazuaki Aihara, Terumasa Aoki (Tohoku Univ) IBISML2013-80 |
In this paper, we propose the Motion dense sampling, which detects very informative interest points from video frames. ... [more] |
IBISML2013-80 pp.95-102 |
| IBISML | 2014-03-07 15:35 |
Nara | Nara Women's University | Extention of Automatic Binary Logistic Estimation model for right censored survival data Toshio Shimokawa (Univ. of Yamanashi) IBISML2013-81 |
An important theme in survival analysis is the investigation of prognosis factors that affect survival time. The tree-st... [more] | IBISML2013-81 pp.103-108 |
| IBISML | 2014-03-07 16:20 |
Nara | Nara Women's University | Binary Principal Points Based on Subgradient Method and Its Application Haruka Yamashita (Keio Univ.), Yoshinobu Kawahara (Osaka Univ.) IBISML2013-82 |
Analysis with Principal Points is a useful statistical tool for summarizing large data. Principal Points is defined as s... [more] | IBISML2013-82 pp.109-115 |
| IBISML | 2014-03-07 16:45 |
Nara | Nara Women's University | Simultaneous estimation of rating and character by a stochastic model of winning percentage Yoshiyuki Nakamoto, Toshiyuki Tanaka (Kyoto Univ.) IBISML2013-83 |
Assuming that a player has a character value in addition to a rate value, we construct a probability density function wh... [more] | IBISML2013-83 pp.117-123 |
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