102 citations · 159 across the 33 of their papers we have counts for
7 papers · 1 filter
Learning Sign-Constrained Support Vector Machines
Kenya Tajima, Takahiko Henmi, Kohei Tsuchida +2
Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning mac…
Adaptive Signal Variances: CNN Initialization Through Modern Architectures
Takahiko Henmi, Esmeraldo Ronnie Rey Zara, Yoshihiro Hirohashi +1
Deep convolutional neural networks (CNN) have achieved the unwavering confidence in its performance on image processing tasks. The CNN architecture constitutes a variety of differe…
Frank-Wolfe algorithm for learning SVM-type multi-category classifiers
Kenya Tajima, Yoshihiro Hirohashi, Esmeraldo Ronnie Rey Zara +1
Multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are lots of variants of MC-SVM, although different optimization algorit…
Parametric Models for Mutual Kernel Matrix Completion
Rachelle Rivero, Tsuyoshi Kato
Recent studies utilize multiple kernel learning to deal with incomplete-data problem. In this study, we introduce new methods that do not only complete multiple incomplete kernel m…
Threshold Auto-Tuning Metric Learning
Yuya Onuma, Rachelle Rivero, Tsuyoshi Kato
It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. A weak point of ITML-based methods is that the distance…
Sign-Constrained Regularized Loss Minimization
Tsuyoshi Kato, Misato Kobayashi, Daisuke Sano
In practical analysis, domain knowledge about analysis target has often been accumulated, although, typically, such knowledge has been discarded in the statistical analysis stage,…
Mutual Kernel Matrix Completion
Tsuyoshi Kato, Rachelle Rivero
With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in h…