Results 1 - 10 of 61,677

Motivational and self-regulated learning components of classroom academic performance

by Paul R. Pintrich, Elisabeth V. De Groot - Journal of Educational Psychology , 1990
"... A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulat ..."
Abstract - Cited by 679 (6 self) - Add to MetaCart
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self

Instance-based learning algorithms

by David W. Aha, Dennis Kibler, Marc K. Albert - Machine Learning , 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
Abstract - Cited by 1389 (18 self) - Add to MetaCart
Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances

Ensemble Methods in Machine Learning

by Thomas G. Dietterich - MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857 , 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract - Cited by 625 (3 self) - Add to MetaCart
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging

NewsWeeder: Learning to Filter Netnews

by Ken Lang - in Proceedings of the 12th International Machine Learning Conference (ML95 , 1995
"... A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest l ..."
Abstract - Cited by 561 (0 self) - Add to MetaCart
level for each article being read (1-5), and then learning a user profile based on these ratings. This paper describes how NewsWeeder accomplishes this task, and examines the alternative learning methods used. The results show that a learning algorithm based on the Minimum Description Length (MDL

Parallel Networks that Learn to Pronounce English Text

by Terrence J. Sejnowski, Charles R. Rosenberg - COMPLEX SYSTEMS , 1987
"... This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
Abstract - Cited by 549 (5 self) - Add to MetaCart
This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed

Unsupervised Learning by Probabilistic Latent Semantic Analysis

by Thomas Hofmann - Machine Learning , 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
Abstract - Cited by 618 (4 self) - Add to MetaCart
Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity

Learning the Kernel Matrix with Semi-Definite Programming

by Gert R. G. Lanckriet, Nello Cristianini, Laurent El Ghaoui, Peter Bartlett, Michael I. Jordan , 2002
"... Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
Abstract - Cited by 775 (21 self) - Add to MetaCart
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information

The role of deliberate practice in the acquisition of expert performance

by K. Anders Ericsson, Ralf Th. Krampe, Clemens Tesch-romer - Psychological Review , 1993
"... The theoretical framework presented in this article explains expert performance as the end result of individuals ' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of ef ..."
Abstract - Cited by 690 (15 self) - Add to MetaCart
for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning. Our civilization has always recognized exceptional individuals, whose performance in sports, the arts, and science is vastly superior

Distance metric learning, with application to clustering with sideinformation,”

by Eric P Xing , Andrew Y Ng , Michael I Jordan , Stuart Russell - in Advances in Neural Information Processing Systems 15, , 2002
"... Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for ..."
Abstract - Cited by 818 (13 self) - Add to MetaCart
us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.

Gradient-based learning applied to document recognition

by Yann Lecun, Léon Bottou, Yoshua Bengio, Patrick Haffner - Proceedings of the IEEE , 1998
"... Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract - Cited by 1533 (84 self) - Add to MetaCart
Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify
Results 1 - 10 of 61,677