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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 |  IBISML2017-35 |
(To be available after the conference date) [more] | IBISML2017-35 pp.1-8 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Extraction of Distribtion Changes using On-line t-SNE Daisuke Kaji (Denso), Masahiro Kobayashi, Kazuho Watanabe (Toyohashi Tech.) IBISML2017-36 |
[more] | IBISML2017-36 pp.9-13 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Calculation method for grid-structured markov random field using corner transfer matrix renormalization group Tomoharu Yoshida, Kazuho Watanabe, Kyoji Umemura (Toyohashi Univ. of Tech.) IBISML2017-37 |
Calculating the marginal distribution of the grid-structured markov random field model in probabilistic image processing... [more] | IBISML2017-37 pp.15-22 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning Naoki Hayashi, Sumio Watanabe (TokyoTech) IBISML2017-38 |
In stochastic matrix factorization (SMF), we deal with problems that we predict an observed stochastic matrix as a produ... [more] | IBISML2017-38 pp.23-30 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Empirical Bayesian Tree Masashi Sekino (SMN) IBISML2017-39 |
We propose a new decision tree learning algorithm ``Empirical Bayesian Tree (EBT)'', which models the outputs of a leaf ... [more] | IBISML2017-39 pp.31-38 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning Tomoya Sakai, Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-40 |
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classif... [more] | IBISML2017-40 pp.39-46 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Application of Transfer Learning to Smallscale Data and Its Evaluation Using Open Datasets Arika Fukushima, Toru Yano, Shuuichiro Imahara, Hideyuki Aisu (Toshiba) IBISML2017-41 |
Large sample size of the training data is essential for high performance of prediction on machine learning. However, in... [more] |
IBISML2017-41 pp.47-53 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Evaluation of Machine Learning Methods for Anomaly Detection Using New Kyoto2006+ Dataset Hisashi Takahara (UNP) IBISML2017-42 |
[more] | IBISML2017-42 pp.55-62 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Material search for Li-ion battery electrolytes by exhaustive search with Gaussian Process modeling Tomofumi Nakayama (Univ. Tokyo), Yasuhiko Igarashi, Keitaro Sodeyama (NIMS), Masato Okada (Univ. Tokyo) IBISML2017-43 |
[more] | IBISML2017-43 pp.63-68 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | [Poster Presentation]
Post Clustering Inference for Heterogeneous Data Shigenori Inoue, Yuta Umezu (NIT), Shoma Tsubota (Nagoya Univ.), Ichiro Takeuchi (NIT/RIKEN/NIMS) IBISML2017-44 |
Along with the prevalence of Precision Medicine, the demand for analytical methods on heterogeneous data is increasing. ... [more] | IBISML2017-44 pp.69-76 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Robust one dimensional phase unwrapping using Markov random fields Yasuhisa Nakashima (Univ. Tokyo), Yasuhiko Igarashi (JST), Yasushi Naruse (NICT), Masato Okada (Univ. Tokyo) IBISML2017-45 |
In the measurement of crustal deformation using satellite or aircraft sensors, interferometric synthetic aperture radar ... [more] | IBISML2017-45 pp.77-84 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Regression Method for Noisy Inputs based on Naradaya-Watson Estimator constructed from Noiseless Training Data Ryo Hanafusa, Takeshi Okadome (Kwansei Gakuin Univ.) IBISML2017-46 |
The regression method proposed in this paper produces a regression function for noisy inputs that minimizes the expected... [more] | IBISML2017-46 pp.85-92 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Online Optimization Method for Generalized $ell_1$ Regularized Problems Yoshihiro Nakazato, Kazuto Fukuchi (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./Riken/JST) IBISML2017-47 |
Structured sparse regularization is vital to enhance the precision and the interpretability of the model by introducing ... [more] | IBISML2017-47 pp.93-100 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | IBISML2017-48 |
[more] | IBISML2017-48 pp.101-107 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Hidden Semi-Markov Model Representing State Overlap for Multiple Sequential Data Input Hiromi Narimatsu, Hiroyuki Kasai (UEC) IBISML2017-49 |
This paper proposes an extended hidden semi-Markov model (HSMM) to handle multiple sequence inputs. The significant limi... [more] | IBISML2017-49 pp.109-114 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Quantum inspired Machine Learning through Entanglement Entropy Masaru Tanaka (Fukuoka UNiv.) IBISML2017-50 |
CNN has been successful in image classification, but we want to focus on the relationship with other fields. In particul... [more] | IBISML2017-50 pp.115-122 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Nonconvex Sparse Optimization with Submodularity Naoki Marumo, Tomoharu Iwata (NTT) IBISML2017-51 |
[more] | IBISML2017-51 pp.123-130 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Regularization of learning prameters in posterior mean estimate approximation in CS-SENSE method Ken Harada, Masato Inoue (Waseda Univ.), Kaori Togashi (Kyoto Univ.) IBISML2017-52 |
We have proposed a method to approximate posterior mean (PM) estimation of the CS-SENSE method, which is one of the tech... [more] | IBISML2017-52 pp.131-138 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Consequently Fair Contextual Bandit Learning Kazuto Fukuchi (Univ. of Tsukuba), Jun Sakuma (Univ. of Tsukuba/JST/RIKEN) IBISML2017-53 |
Fairness in machine learning is being recognized as an important field. It requires that the consequent decisions made b... [more] | IBISML2017-53 pp.139-146 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | IBISML2017-54 |
(To be available after the conference date) [more] | IBISML2017-54 pp.147-153 |
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