Results 1 - 10 of 7,738

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches, including decision trees, are given. As a demonstration

Effects with Random Assignment: Results for Dartmouth Roommates

by Bruce Sacerdote , 2001
"... This paper uses a unique data set to measure peer effects among college roommates. Freshman year roommates and dormmates are randomly assigned at Dartmouth College. I find that peers have an impact on grade point average and on decisions to join social groups such as fraternities. Residential peer e ..."
Abstract - Cited by 554 (6 self) - Add to MetaCart
This paper uses a unique data set to measure peer effects among college roommates. Freshman year roommates and dormmates are randomly assigned at Dartmouth College. I find that peers have an impact on grade point average and on decisions to join social groups such as fraternities. Residential peer

An experimental comparison of three methods for constructing ensembles of decision trees

by Thomas G. Dietterich, Doug Fisher - Bagging, boosting, and randomization. Machine Learning , 2000
"... Abstract. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base ” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approac ..."
Abstract - Cited by 610 (6 self) - Add to MetaCart
approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance

Random Early Detection Gateways for Congestion Avoidance.

by Sally Floyd , Van Jacobson - IEEELACM Transactions on Networking, , 1993
"... Abstract-This paper presents Random Early Detection (RED) gateways for congestion avoidance in packet-switched networks. The gateway detects incipient congestion by computing the average queue size. The gateway could notify connections of congestion either by dropping packets arriving at the gatewa ..."
Abstract - Cited by 2716 (31 self) - Add to MetaCart
Abstract-This paper presents Random Early Detection (RED) gateways for congestion avoidance in packet-switched networks. The gateway detects incipient congestion by computing the average queue size. The gateway could notify connections of congestion either by dropping packets arriving

High confidence visual recognition of persons by a test of statistical independence

by John G. Daugman - IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1993
"... A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the ..."
Abstract - Cited by 621 (8 self) - Add to MetaCart
of the human population reveals variation corresponding to several hundred independent degrees-of-freedom. Morphogenetic randomness in the texture expressed phenotypically in the iris trabecular meshwork ensures that a test of statistical independence on two coded patterns originating from different eyes

A theory of memory retrieval

by Roger Ratcliff - PSYCHOL. REV , 1978
"... A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probe-memory item relatedness, just as a ringing tuning fork evokes sympath ..."
Abstract - Cited by 769 (83 self) - Add to MetaCart
sympathetic vibrations in other tuning forks. Evidence is accumulated in parallel from each probe-memory item comparison, and each comparison is modeled by a continuous random walk process. In item recognition, the decision process is self-terminating on matching comparisons and exhaustive on nonmatching

Experiments with a New Boosting Algorithm

by Yoav Freund, Robert E. Schapire , 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
Abstract - Cited by 2213 (20 self) - Add to MetaCart
In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced

Network Coding for Large Scale Content Distribution

by Christos Gkantsidis, Pablo Rodriguez Rodriguez
"... We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling of bloc ..."
Abstract - Cited by 493 (7 self) - Add to MetaCart
We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling

On the time course of perceptual choice: the leaky competing accumulator model

by Marius Usher, James L. McClelland - PSYCHOLOGICAL REVIEW , 2001
"... The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. In special cases, the model becomes equivalent to a classical diffus ..."
Abstract - Cited by 480 (19 self) - Add to MetaCart
The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. In special cases, the model becomes equivalent to a classical

Probabilistic Characterization of Random Decision Trees

by Amit Dhurandhar, Alin Dobra, Greg Ridgeway
"... In this paper we use the methodology introduced by Dhurandhar and Dobra (2009) for analyzing the error of classifiers and the model selection measures, to analyze decision tree algorithms. The methodology consists of obtaining parametric expressions for the moments of the generalization error (GE) f ..."
Abstract - Cited by 6 (6 self) - Add to MetaCart
contributions we make in this paper are: (a) we primarily characterize a subclass of decision trees namely, Random decision trees, (b) we discuss how the analysis extends to other decision tree algorithms and (c) in order to extend the analysis to certain model selection measures, we generalize
Results 1 - 10 of 7,738