probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class is weakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions

seemingly small fraction of U.S. GDP. This is not surprising in view of the fact that large economies trade less with others, and more internally. But the modest share of trade in total national income hides the fact that merchandise trade as a share of merchandise value-added is quite high for the U

fraction of the nodes crash or the network partitions. But current overlays are not secure; even a small fraction of malicious nodes can prevent correct message delivery throughout the overlay. This prob-lem is particularly serious in open peer-to-peer systems, where many diverse, autonomous parties

is highly sensitive to the stride of data accesses and the size of the blocks, and can cause wide variations in machine performance for different matrix sizes. The conventional wisdom of trying to use the entire cache, or even a fixed fraction of the cache, is incorrect. If a fixed block size is used for a

unjustifiably, leading to flawed results.This assumption of normal distribution limits the ability to analyze rare events. Unfortunately rare events do make up a large fraction of real text.However, more applicable methods based on likelihood ratio tests are available that yield good results with relatively

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. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts

Centre d’Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities

from spam. We first select a small set of seed pages to be evaluated by an expert. Once we manually identify the reputable seed pages, we use the link structure of the web to discover other pages that are likely to be good. In this paper we discuss possible ways to implement the seed selection

This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering