K
Koby Crammer
Researcher at Technion – Israel Institute of Technology
Publications - 143
Citations - 19305
Koby Crammer is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Online algorithm & Support vector machine. The author has an hindex of 45, co-authored 141 publications receiving 17155 citations. Previous affiliations of Koby Crammer include Interdisciplinary Center for Neural Computation & University of Pennsylvania.
Papers
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A theory of learning from different domains
Shai Ben-David,John Blitzer,Koby Crammer,Alex Kulesza,Fernando Pereira,Jennifer Wortman Vaughan +5 more
TL;DR: A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.
Journal Article
On the algorithmic implementation of multiclass kernel-based vector machines
Koby Crammer,Yoram Singer +1 more
TL;DR: This paper describes the algorithmic implementation of multiclass kernel-based vector machines using a generalized notion of the margin to multiclass problems, and describes an efficient fixed-point algorithm for solving the reduced optimization problems and proves its convergence.
Proceedings Article
Analysis of Representations for Domain Adaptation
TL;DR: The theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set.
Journal Article
Online Passive-Aggressive Algorithms
TL;DR: This work presents a unified view for online classification, regression, and uni-class problems, and proves worst case loss bounds for various algorithms for both the realizable case and the non-realizable case.
Proceedings Article
Online Passive-Aggressive Algorithms
TL;DR: In this article, a unified view for online classification, regression, and uni-class problems is presented, which leads to a single algorithmic framework for the three problems, and the authors prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case.