Results 11 - 20 of 77,434

Improving generalization with active learning

by David Cohn, Richard Ladner, Alex Waibel - Machine Learning , 1994
"... Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples ..."
Abstract - Cited by 544 (1 self) - Add to MetaCart
alone, giving better generalization for a fixed number of training examples. In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a

Some studies in machine learning using the game of Checkers

by Arthur L. Samuel - IBM JOURNAL OF RESEARCH AND DEVELOPMENT , 1959
"... Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program. Furthermor ..."
Abstract - Cited by 780 (0 self) - Add to MetaCart
Two machine-learning procedures have been investigated in some detail using the game of checkers. Enough work has been done to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program

Reinforcement learning: a survey

by Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore - Journal of Artificial Intelligence Research , 1996
"... This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem ..."
Abstract - Cited by 1714 (25 self) - Add to MetaCart
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning

Transductive Inference for Text Classification using Support Vector Machines

by Thorsten Joachims , 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
Abstract - Cited by 892 (4 self) - Add to MetaCart
This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try

Semi-Supervised Learning Literature Survey

by Xiaojin Zhu , 2006
"... We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter ..."
Abstract - Cited by 782 (8 self) - Add to MetaCart
We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a

The Alignment Template Approach to Statistical Machine Translation

by Franz Josef Och, Hermann Ney , 2004
"... A phrase-based statistical machine translation approach — the alignment template approach — is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order f ..."
Abstract - Cited by 480 (26 self) - Add to MetaCart
from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source–channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe

Ontology Learning for the Semantic Web

by Er Maedche, Steffen Staab - IEEE Intelligent Systems , 2001
"... The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering o ..."
Abstract - Cited by 492 (16 self) - Add to MetaCart
. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-To-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer

Thumbs up? Sentiment Classification using Machine Learning Techniques

by Bo Pang, Lillian Lee, Shivakumar Vaithyanathan - IN PROCEEDINGS OF EMNLP , 2002
"... We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three mac ..."
Abstract - Cited by 1101 (7 self) - Add to MetaCart
We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

by Thorsten Joachims , 1998
"... This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substan ..."
Abstract - Cited by 2303 (9 self) - Add to MetaCart
This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve

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
Results 11 - 20 of 77,434