• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 12,821
Next 10 →

Independent Learning

by unknown authors
"... This action research discusses how the teacher integrates the technology-based Self-access Center and Web-based environments into an integrated language skills English class at a technological university in Taiwan. In this study, Web-based environments include English learning Web sites, and communi ..."
Abstract - Add to MetaCart
students through interviews, reflective diaries and a questionnaire given out at the end of the term. Collected data indicated that although difficulties and problems were encountered, students maintained a positive attitude toward independent learning in various technology-based learning environments

Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval

by David D. Lewis , 1998
"... The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made abou ..."
Abstract - Cited by 499 (1 self) - Add to MetaCart
The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made

The "Independent Components" of Natural Scenes are Edge Filters

by Anthony J. Bell, Terrence J. Sejnowski , 1997
"... It has previously been suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and it has been reasoned that such responses should emerge from an unsupervised learning algorithm that attem ..."
Abstract - Cited by 617 (29 self) - Add to MetaCart
that attempts to find a factorial code of independent visual features. We show here that a new unsupervised learning algorithm based on information maximization, a nonlinear "infomax" network, when applied to an ensemble of natural scenes produces sets of visual filters that are localized and oriented

What Can Economists Learn from Happiness Research?

by Bruno S. Frey, Alois Stutzer - FORTHCOMING IN JOURNAL OF ECONOMIC LITERATURE , 2002
"... Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a self-evident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows that e ..."
Abstract - Cited by 545 (24 self) - Add to MetaCart
Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a self-evident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows

Learning and Computers for Independent Learning

by Author(s Fong N
"... Abstract. This presentation is going to analyse the good practices of the alternating use of small group learning and independent learning with computers in a second language classroom. It was a weekly Reading and Phonics lesson scheduled in the Language Learning room for the lower form of primary s ..."
Abstract - Add to MetaCart
Abstract. This presentation is going to analyse the good practices of the alternating use of small group learning and independent learning with computers in a second language classroom. It was a weekly Reading and Phonics lesson scheduled in the Language Learning room for the lower form of primary

The use of the area under the ROC curve in the evaluation of machine learning algorithms

by Andrew P. Bradley - PATTERN RECOGNITION , 1997
"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Ne ..."
Abstract - Cited by 685 (3 self) - Add to MetaCart
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k

Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm

by Nick Littlestone - Machine Learning , 1988
"... learning Boolean functions, linear-threshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
Abstract - Cited by 773 (5 self) - Add to MetaCart
algorithms are available that make a bounded number of mistakes, with the bound independent of the number of examples seen by the learner. We present one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions. The basic method can

Learnability in Optimality Theory

by Bruce Tesar, Paul Smolensky , 1995
"... In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given gr ..."
Abstract - Cited by 529 (35 self) - Add to MetaCart
In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given

Bayesian Network Classifiers

by Nir Friedman, Dan Geiger, Moises Goldszmidt , 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
Abstract - Cited by 796 (20 self) - Add to MetaCart
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less

On the optimality of the simple Bayesian classifier under zero-one loss

by Pedro Domingos, Michael Pazzani - MACHINE LEARNING , 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
Abstract - Cited by 818 (27 self) - Add to MetaCart
range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers
Next 10 →
Results 1 - 10 of 12,821
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University