Results 1 - 10 of 107,270

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 13236 (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

Proposed NIST Standard for Role-Based Access Control

by David F. Ferraiolo, Ravi Sandhu, Serban Gavrila, D. Richard Kuhn, Ramaswamy Chandramouli , 2001
"... In this article we propose a standard for role-based access control (RBAC). Although RBAC models have received broad support as a generalized approach to access control, and are well recognized for their many advantages in performing large-scale authorization management, no single authoritative def ..."
Abstract - Cited by 544 (13 self) - Add to MetaCart
In this article we propose a standard for role-based access control (RBAC). Although RBAC models have received broad support as a generalized approach to access control, and are well recognized for their many advantages in performing large-scale authorization management, no single authoritative

Smooth minimization of nonsmooth functions

by Yu. Nesterov - Math. Programming , 2005
"... In this paper we propose a new approach for constructing efficient schemes for nonsmooth convex optimization. It is based on a special smoothing technique, which can be applied to the functions with explicit max-structure. Our approach can be considered as an alternative to black-box minimization. F ..."
Abstract - Cited by 523 (1 self) - Add to MetaCart
In this paper we propose a new approach for constructing efficient schemes for nonsmooth convex optimization. It is based on a special smoothing technique, which can be applied to the functions with explicit max-structure. Our approach can be considered as an alternative to black-box minimization

An integrative theory of prefrontal cortex function.

by Earl K Miller , Jonathan D Cohen - Annual Review of Neuroscience, , 2001
"... Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active ..."
Abstract - Cited by 1093 (20 self) - Add to MetaCart
Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

by Xiaojin Zhu , Zoubin Ghahramani, John Lafferty - IN ICML , 2003
"... An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
Abstract - Cited by 752 (14 self) - Add to MetaCart
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning

Performance Analysis of the IEEE 802.11 Distributed Coordination Function

by Giuseppe Bianchi , 2000
"... Recently, the IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary slott ..."
Abstract - Cited by 1869 (1 self) - Add to MetaCart
Recently, the IEEE has standardized the 802.11 protocol for Wireless Local Area Networks. The primary medium access control (MAC) technique of 802.11 is called distributed coordination function (DCF). DCF is a carrier sense multiple access with collision avoidance (CSMA/CA) scheme with binary

A model for technical inefficiency effects in a stochastic frontier production function for panel data

by G. E. Battese - Empirical Economics , 1995
"... Abstract: A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inetGciency effects are assumed to be a function of firm-specific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of nor ..."
Abstract - Cited by 555 (4 self) - Add to MetaCart
of normal distributions with constant variance, but with means which are a linear function of observable variables. This panel data model is an extension of recently proposed models for inefTiciency effects in stochastic frontiers for cross-sectional data. An empirical application of the model is obtained

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

by Jianqing Fan , Runze Li , 2001
"... Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
Abstract - Cited by 948 (62 self) - Add to MetaCart
functions are symmetric, nonconcave on (0, ∞), and have singularities at the origin to produce sparse solutions. Furthermore, the penalty functions should be bounded by a constant to reduce bias and satisfy certain conditions to yield continuous solutions. A new algorithm is proposed for optimizing

Learning to rank using gradient descent

by Chris Burges, Tal Shaked, Erin Renshaw, Matt Deeds, Nicole Hamilton, Greg Hullender - In ICML , 2005
"... We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data f ..."
Abstract - Cited by 534 (17 self) - Add to MetaCart
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data

Information Theory and Statistics

by S. Kullback , 1968
"... Entropy and relative entropy are proposed as features extracted from symbol sequences. Firstly, a proper Iterated Function System is driven by the sequence, producing a fractaMike representation (CSR) with a low computational cost. Then, two entropic measures are applied to the CSR histogram of th ..."
Abstract - Cited by 1805 (2 self) - Add to MetaCart
Entropy and relative entropy are proposed as features extracted from symbol sequences. Firstly, a proper Iterated Function System is driven by the sequence, producing a fractaMike representation (CSR) with a low computational cost. Then, two entropic measures are applied to the CSR histogram
Results 1 - 10 of 107,270