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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract - Cited by 12976 (32 self) - Add to MetaCart
on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both

A Tool for Estimating Information Leakage ⋆

by Tom Chothia, Yusuke Kawamoto, Chris Novakovic
"... Abstract. We present leakiEst, a tool that estimates how much information leaks from systems. To use leakiEst, an analyst must run a system with a range of secret values and record the outputs that may be exposed to an attacker. Our tool then estimates the amount of information leaked from the secre ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
Abstract. We present leakiEst, a tool that estimates how much information leaks from systems. To use leakiEst, an analyst must run a system with a range of secret values and record the outputs that may be exposed to an attacker. Our tool then estimates the amount of information leaked from

Language-Based Information-Flow Security

by Andrei Sabelfeld , Andrew C. Myers - IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS , 2003
"... Current standard security practices do not provide substantial assurance that the end-to-end behavior of a computing system satisfies important security policies such as confidentiality. An end-to-end confidentiality policy might assert that secret input data cannot be inferred by an attacker throug ..."
Abstract - Cited by 821 (57 self) - Add to MetaCart
Current standard security practices do not provide substantial assurance that the end-to-end behavior of a computing system satisfies important security policies such as confidentiality. An end-to-end confidentiality policy might assert that secret input data cannot be inferred by an attacker

Estimation and Inference in Econometrics

by James G. Mackinnon , 1993
"... The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o ..."
Abstract - Cited by 1151 (3 self) - Add to MetaCart
The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case.

Modern Information Retrieval

by Ricardo Baeza-Yates, Berthier Ribeiro-Neto , 1999
"... Information retrieval (IR) has changed considerably in the last years with the expansion of the Web (World Wide Web) and the advent of modern and inexpensive graphical user interfaces and mass storage devices. As a result, traditional IR textbooks have become quite out-of-date which has led to the i ..."
Abstract - Cited by 3155 (28 self) - Add to MetaCart
Information retrieval (IR) has changed considerably in the last years with the expansion of the Web (World Wide Web) and the advent of modern and inexpensive graphical user interfaces and mass storage devices. As a result, traditional IR textbooks have become quite out-of-date which has led

Wrapper Induction for Information Extraction

by Nicholas Kushmerick , 1997
"... The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually ..."
Abstract - Cited by 612 (30 self) - Add to MetaCart
The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources

Alignment by Maximization of Mutual Information

by Paul A. Viola , 1995
"... ..."
Abstract - Cited by 1009 (13 self) - Add to MetaCart
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LabelMe: A Database and Web-Based Tool for Image Annotation

by B. C. Russell, A. Torralba, K. P. Murphy, W. T. Freeman , 2008
"... We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sha ..."
Abstract - Cited by 670 (47 self) - Add to MetaCart
We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant

A Language Modeling Approach to Information Retrieval

by Jay M. Ponte, W. Bruce Croft , 1998
"... Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. We argue that much of the reason for this is the lack of an adequate indexing model. This sugg ..."
Abstract - Cited by 1142 (40 self) - Add to MetaCart
an approach to retrieval based on probabilistic language modeling. We estimate models for each document individually. Our approach to modeling is non-parametric and integrates document indexing and document retrieval into a single model. One advantage of our approach is that collection statistics which

Estimating Continuous Distributions in Bayesian Classifiers

by George John, Pat Langley - In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence , 1995
"... When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality ..."
Abstract - Cited by 489 (2 self) - Add to MetaCart
distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models. In Proceedings of the Eleventh Conference on Uncertainty
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