R
Ran El-Yaniv
Researcher at Technion – Israel Institute of Technology
Publications - 138
Citations - 14744
Ran El-Yaniv is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Support vector machine & Competitive analysis. The author has an hindex of 40, co-authored 133 publications receiving 12684 citations. Previous affiliations of Ran El-Yaniv include University of Toronto & Google.
Papers
More filters
Proceedings Article
Can We Learn to Beat the Best Stock
TL;DR: The empirical results on historical markets provide strong evidence that this type of technical trading can "beat the market" and moreover, can beat the best stock in the market.
Posted Content
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman,Ran El-Yaniv +1 more
TL;DR: This work considers the problem of selective prediction in deep neural networks, and introduces SelectiveNet, a deep neural architecture with an integrated reject option that is trained to optimize both classification (or regression) and rejection simultaneously, end-to-end.
Proceedings ArticleDOI
On feature distributional clustering for text categorization
TL;DR: This work describes a text categorization approach that is based on a combination of feature distributional clusters with a support vector machine (SVM) classifier that yields high performance text classification that can outperform other recent methods in terms of categorization accuracy and representation efficiency.
Patent
Methods and systems of supervised learning of semantic relatedness
Ran El-Yaniv,David Yanay +1 more
TL;DR: In this article, a method of evaluating a semantic relatedness of terms is proposed, which comprises providing a plurality of text segments, calculating, using a processor, a pluralityof weights each for another of the plurality of texts, calculating a prevalence of a co-appearance of each of the pairs of terms in the plurality, and evaluating the relatedness between members of each pair according to a combination of a respective the prevalence and a weight of each text segment wherein a coappearance occurs.
Proceedings ArticleDOI
Multi-way distributional clustering via pairwise interactions
TL;DR: An extensive empirical study of two-way, three-way and four-way applications of the MDC scheme using six real-world datasets including the 20 News-groups and the Enron email collection shows that the algorithms consistently and significantly outperform previous state-of-the-art information theoretic clustering algorithms.