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We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradientbased learning. Metaphorically, the adaptation allows us to find needles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems from recent(More)
In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function. Unlike most of previous approaches which(More)
We describe and analyze a simple and effective iterative algorithm for solving the optimization problem cast by Support Vector Machines (SVM). Our method alternates between stochastic gradient descent steps and projection steps. We prove that the number of iterations required to obtain a solution of accuracy ε is Õ(1/ε). In contrast, previous(More)
We present a unified view for online classification, regression, and uniclass problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. A conversion of our main online algorithm to the setting of batch learning is also(More)
Previous studies have shown that a recombinant vaccine expressing four highly conserved influenza virus epitopes has a potential for a broad spectrum, cross-reactive vaccine; it induced protection against H1, H2 and H3 influenza strains. Here, we report on the evaluation of an epitope-based vaccine in which six conserved epitopes, common to many influenza(More)
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance(More)
We describe efficient algorithms for projecting a vector onto the <i>l</i><sub>1</sub>-ball. We present two methods for projection. The first performs exact projection in <i>O(n)</i> expected time, where <i>n</i> is the dimension of the space. The second works on vectors <i>k</i> of whose elements are perturbed outside the <i>l</i><sub>1</sub>-ball,(More)
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv)(More)
We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of(More)
BACKGROUND The prevalence of chronic pain in the general population ranges from 10% to over 40%, depending on the definition and the population studied. No large study has been conducted in Israel. OBJECTIVES To evaluate the prevalence of patients with chronic pain, and characterize them in a large community random sample. METHODS We conducted a survey(More)