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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2022-09-15
Kanagawa Keio Univ. (Yagami Campus)
(Primary: On-site, Secondary: Online)
Improving Efficiency of Regularization Path Computation in Safe Pattern Pruning via Multiple Referential Solutions
Takumi Yoshida (Nitech), Hiroyuki Hanada (RIKEN), Kazuya Nakagawa, Shinya Suzumura, Onur Boyar, Kazuki Iwata (Nitech), Shun Shimura, Yuji Tanaka (NaogyaU), Masayuki Karasuyama (Nitech), Kouichi Taji (NaogyaU), Koji Tsuda (UTokyo/RIKEN), Ichiro Takeuchi (NaogyaU/RIKEN) IBISML2022-38
Safe Screening and Safe Pattern Pruning are methods for efficiently modeling high-dimensional features by $L_1$-regulari... [more] IBISML2022-38
IBISML 2017-03-07
Tokyo Tokyo Institute of Technology Safe Pruning Rule for Predictive Sequential Pattern Mining and Its Application to Bio-logging Data Analysis
Kaoru Kishimoto (NITech), Masayuki Karasuyama (NIT), Kazuya Nakagawa (NITech), Kotaro Kimura (Osaka Univ.), Ken Yoda (Nagoya Univ.), Yuta Umezu, Shinsuke Kajioka (NITech), Koji Tsuda (UTokyo), Ichiro Takeuchi (NITech) IBISML2016-105
Recently, the analysis for time-series logging data of animal behaviors, called bio-logging data, has attracted a wide a... [more] IBISML2016-105
IBISML 2016-11-16
Kyoto Kyoto Univ. Selective Inference for High-Dimensional Binary Classification
Yuta Umezu, Kazuya Nakagawa (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) IBISML2016-59
In machine learning and other related area, the number of features is often reduced by some feature selection procedure ... [more] IBISML2016-59
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-05
Toyama   [Short Paper] Selective Inference for Time-series Change-Point Analysis
Yuta Umezu, Kazuya Nakagawa, Shigenori Inoue (NIT), Koji Tsuda (Tokyo Univ.), Mahito Sugiyama, Takuya Maekawa (Osaka Univ.), Toru Tamaki (Hiroshima Univ.), Ken Yoda (Nagoya Univ.), Ichiro Takeuchi (NIT) PRMU2016-63 IBISML2016-18
In this paper, we propose a statistical method for time series data after detecting a change point. Because the change p... [more] PRMU2016-63 IBISML2016-18
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-05
Toyama   Sparse learning for pattern mining problem by using Safe Pattern Pruning method
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) PRMU2016-70 IBISML2016-25
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a s... [more] PRMU2016-70 IBISML2016-25
(Joint) [detail]
Okinawa Okinawa Institute of Science and Technology Efficient sparse learning for combinatorial model by using safe screening approach
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama, Ichiro Takeuchi (NIT) IBISML2015-10
In a variety of machine learning tasks, it has been desired to incorporate high-order interaction effects of multiple co... [more] IBISML2015-10
(Joint) [detail]
Okinawa Okinawa Institute of Science and Technology Selective inference for high-order interaction model
Shinya Suzumura, Kazuya Nakagawa (NIT), Koji Tsuda (UT), Ichiro Takeuchi (NIT) IBISML2015-11
Finding statistically significant high-order interaction features in predictive modeling is important but challenging ta... [more] IBISML2015-11
Kumamoto Kumamoto University Robust Detection of Wet Road Condition using Polarization for Sunlight
Kazuya Nakagawa, Keiji Shibata, Yuukou Horita (Univ. of Toyama) ITS2009-18
Various methods have already been proposed in the field of road detecting condition. Detecting road wet condition using ... [more] ITS2009-18
 Results 1 - 8 of 8  /   
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