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Technical Committee on Information-Based Induction Sciences and Machine Learning (IBISML) (Searched in: 2017)
Search Results: Keywords 'from:2017-11-08 to:2017-11-08'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Evaluation of KL-divergence between Gaussian process posteriors by finite dimensional normal distributions Hideaki Ishibashi, Tetsuo Furukawa (Kyutech), Shotaro Akaho (AIST)  IBISML2017-55 |
[more] | IBISML2017-55 pp.155-160 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Clinical Decision Support System based on Co-occurrence Analysis of Disease Names
-- Utilization of Clinical Big Text Data by Artificial Intelligence -- Ken Yano, Eiji Aramaki (NAIST) IBISML2017-56 |
Medical information such as case reports describe the systematic documentation of a patient's medical history and care a... [more] | IBISML2017-56 pp.161-168 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Efficient modal regression using gradient ascent and descent method Ryoya Yamasaki, Toshiyuki Tanaka (Kyoto Univ.) IBISML2017-57 |
Nonparametric modal regression is a method for regression analysis, which can estimate local maxima of the conditional p... [more] | IBISML2017-57 pp.169-176 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Learning huge Bayesian networks using RAI algorithm based on Bayes factor Kazuki Natori, Masaki Uto, Maomi Ueno (UEC) IBISML2017-58 |
``Learning Bayesian networks'' has NP-hard problem. The state-of-the-arts method of learning Bayesian networks cannot le... [more] | IBISML2017-58 pp.177-184 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Bayesian nonparametric model for uncountable factor analysis Masahiro Nakano (NTT), Daichi Mochihashi, Tomoko Matsui (ISM), Kunio Kashino (NTT) IBISML2017-59 |
[more] | IBISML2017-59 pp.185-192 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Fast Computation of Lower Bounds for Privacy Evaluations, Based on Binary Decision Diagrams Shogo Takeuchi (Univ. of Tokyo), Kosuke Kusano, Jun Sakuma (Univ. of Tsukuba), Koji Tsuda (Univ. of Tokyo) IBISML2017-60 |
An input value estimation is a privacy issue in a service provides information by personal information. It is necessary ... [more] | IBISML2017-60 pp.193-200 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | IBISML2017-72 |
We propose an accelerated best-first search (BFS) for monotone submodular function maximization with a knapsack constrai... [more] | IBISML2017-72 pp.277-282 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Analysis of Dropout in online learning Kazuyuki Hara (Nihon Univ.) IBISML2017-61 |
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning... [more] |
IBISML2017-61 pp.201-206 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Binary Classification from Positive-Confidence Data Takashi Ishida (SMAM/UTokyo/RIKEN), Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-62 |
Reducing labeling costs in supervised learning is a critical issue in many practical machine learning applications. In ... [more] | IBISML2017-62 pp.207-214 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Rotated Image Recognition by Continuous execution of Angle Estimation Using a Feature Map with CNN Nishiki Katayama, Satoshi Yamane (Kanazawa Univ.) IBISML2017-63 |
Feature extraction by using a Convolutional Neural Network (CNN) has made remarkable results in general object recogniti... [more] | IBISML2017-63 pp.215-218 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Safe Screening for Large Margin Metric Learning Tomoki Yoshida (NITech), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST) IBISML2017-64 |
Large margin metric learning learns the optimal Mahalanobis distance for classification problem based on the margin maxi... [more] | IBISML2017-64 pp.219-226 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Convolutional Neural Network with Entanglement Entropy Shu Eguchi, Masaru Tanaka (Fukuoka Univ.) IBISML2017-65 |
In this paper, we propose a CNN with a process to reduce unnecessary information to identify input data using entangleme... [more] | IBISML2017-65 pp.227-233 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction Hirotaka Kaji, Hayato Yamaguchi (Toyota Motor), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-66 |
In real-world machine learning applications, we are often faced with a situation where only a small number of training s... [more] | IBISML2017-66 pp.235-242 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Hierarchical Reinforcement Learning Based on Return-Weighted Density Estimation Takayuki Osa (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-67 |
We propose a hierarchical reinforcement learning (HRL) methods for learning the optimal policy from a multi-modal reward... [more] | IBISML2017-67 pp.243-249 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Improvement of learning method with convolutional restricted Boltzmann machine based on PCD Ryosuke Ishi, Reiki Suda, Koujin Takeda (Ibaraki Univ.) IBISML2017-68 |
[more] | IBISML2017-68 pp.251-254 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Bin width optimization of multidimensional histogram on count data Kensuke Muto, Hirotaka Sakamoto, Keisuke Matsuura, Takahisa Arima, Masato Okada (Tokyo Univ.) IBISML2017-69 |
A large amount of 4-dimensional count data are obtained by inelastic neutron scattering experiments conducted by chopper... [more] | IBISML2017-69 pp.255-260 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Nonlinear parametric model based on power law for tsunami height prediction Masashi Yoshikawa (UT), Yasuhiko Igarashi (JST), Shin Murata (UT), Toshitaka Baba (TU), Takane Hori (JAMSTEC), Masato Okada (UT) IBISML2017-70 |
When a subduction-zone earthquake occurs, we need to predict the tsunami height in order to cope with the tsunami damage... [more] | IBISML2017-70 pp.261-267 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Selective Inference for Change Point Detection in Multidimensional Sequence Yuta Umezu (Nitech), Ichiro Takeuchi (Nitech/RIKEN/NIMS) IBISML2017-71 |
In various fields such as engineering, bioinformatics and econometrics, detecting structural changes from a given sequen... [more] | IBISML2017-71 pp.269-276 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Compressed Sensing CT image reconstruction using Bayesian Optimization for mixing multiple image priors Tomonori Suga, Masato Inoue (Waseda Univ.) IBISML2017-73 |
In order to reduce the amount of radiation exposure, which increases the risk of cancer, many researches have been done ... [more] | IBISML2017-73 pp.283-288 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Correcting selection bias in active learning based on selective inference framework Yu Inatsu (RIKEN), Ichiro Takeuchi (Nitech/RIKEN/NIMS) IBISML2017-74 |
Consider the active learning that constructs regression model from given data and actually observes the value at the po... [more] | IBISML2017-74 pp.289-296 |
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