Results 1 - 10 of 40,161

Separation Logic: A Logic for Shared Mutable Data Structures

by John Reynolds , 2002
"... In joint work with Peter O'Hearn and others, based on early ideas of Burstall, we have developed an extension of Hoare logic that permits reasoning about low-level imperative programs that use shared mutable data structure. ..."
Abstract - Cited by 950 (6 self) - Add to MetaCart
In joint work with Peter O'Hearn and others, based on early ideas of Burstall, we have developed an extension of Hoare logic that permits reasoning about low-level imperative programs that use shared mutable data structure.

Blind Signal Separation: Statistical Principles

by Jean-Francois Cardoso , 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
Abstract - Cited by 529 (4 self) - Add to MetaCart
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption

Rendering of Surfaces from Volume Data

by Marc Levoy - IEEE COMPUTER GRAPHICS AND APPLICATIONS , 1988
"... The application of volume rendering techniques to the display of surfaces from sampled scalar functions of three spatial dimensions is explored. Fitting of geometric primitives to the sampled data is not required. Images are formed by directly shading each sample and projecting it onto the picture ..."
Abstract - Cited by 875 (12 self) - Add to MetaCart
The application of volume rendering techniques to the display of surfaces from sampled scalar functions of three spatial dimensions is explored. Fitting of geometric primitives to the sampled data is not required. Images are formed by directly shading each sample and projecting it onto

Combining labeled and unlabeled data with co-training

by Avrim Blum, Tom Mitchell , 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
Abstract - Cited by 1633 (28 self) - Add to MetaCart
data, but our goal is to use both views together to allow inexpensive unlabeled data to augment amuch smaller set of labeled examples. Speci cally, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each

Calibrating noise to sensitivity in private data analysis

by Cynthia Dwork, Frank Mcsherry, Kobbi Nissim, Adam Smith - In Proceedings of the 3rd Theory of Cryptography Conference , 2006
"... Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the datab ..."
Abstract - Cited by 649 (60 self) - Add to MetaCart
obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive. 1 Introduction We continue a line of research initiated in [10, 11] on privacy in statistical data-bases. A statistic is a quantity computed from a sample. Intuitively, if the database is a

A tutorial on support vector machines for pattern recognition

by Christopher J. C. Burges - Data Mining and Knowledge Discovery , 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
Abstract - Cited by 3393 (12 self) - Add to MetaCart
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when

Support-Vector Networks

by Corinna Cortes, Vladimir Vapnik - Machine Learning , 1995
"... The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
Abstract - Cited by 3703 (35 self) - Add to MetaCart
properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the supportvector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.

Secure Routing for Mobile Ad Hoc Networks

by Panagiotis Papadimitratos, Zygmunt Haas - MOBILE COMPUTING AND COMMUNICATIONS REVIEW , 2002
"... For such self-organizing infrastructures as mobile ad hoc networks , envisioned to operate in an open, collaborative, and highly volatile environment, the importance of secu- rity cannot be underrated. The provision of comprehen- sive secure communication mandates that both route dis- covery and dat ..."
Abstract - Cited by 599 (14 self) - Add to MetaCart
and data forwarding be safeguarded. The discussed here Secure Routing Protocol (SRP) [1] counters malicious behavior that targets the discovery of topological informa- tion. The protection of the data transmission is a separate problem: an intermittently misbehaving attacker could first comply

Multivariate adaptive regression splines

by Jerome H. Friedman - The Annals of Statistics , 1991
"... A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automaticall ..."
Abstract - Cited by 700 (2 self) - Add to MetaCart
A new method is presented for flexible regression modeling of high dimensional data. The model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations

An analysis of transformations

by G. E. P. Box, D. R. Cox - Journal of the Royal Statistical Society. Series B (Methodological , 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
Abstract - Cited by 1067 (3 self) - Add to MetaCart
In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal
Results 1 - 10 of 40,161