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pro vyhledávání: '"Suzumura, Shinya"'
Autor:
Suzumura, Shinya, Abe, Hitoshi
In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that is pr
Externí odkaz:
http://arxiv.org/abs/2206.03853
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns ar
Externí odkaz:
http://arxiv.org/abs/1602.04601
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SP
Externí odkaz:
http://arxiv.org/abs/1602.04548
In support vector machine (SVM) applications with unreliable data that contains a portion of outliers, non-robustness of SVMs often causes considerable performance deterioration. Although many approaches for improving the robustness of SVMs have been
Externí odkaz:
http://arxiv.org/abs/1507.03229
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to be considered would be extreme
Externí odkaz:
http://arxiv.org/abs/1506.08002
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on
Externí odkaz:
http://arxiv.org/abs/1401.6740
Akademický článek
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Complex-Valued Multilayer Perceptron Search Utilizing Eigen Vector Descent and Reducibility Mapping.
Autor:
Suzumura, Shinya, Nakano, Ryohei
Publikováno v:
Artificial Neural Networks & Machine Learning - ICANN 2012 (9783642332654); 2012, p1-8, 8p
