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6,224
Learning from ambiguously labeled examples
- Intell. Data Anal
, 2006
"... Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned t ..."
Abstract
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Cited by 16 (1 self)
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Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned
Filtering noisy continuous labeled examples †
"... Abstract. It is common in Machine Learning where rules are learned from examples that some of them could not be informative, otherwise they could be irrelevant or noisy. This type of examples makes the Machine Learning Systems produce not adequate rules. In this paper we present an algorithm that fi ..."
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that filters noisy continuous labeled examples, whose computational cost is O(N·logN+NA 2) for N examples and A attributes. Besides, it is shown experimentally to be better than the embedded algorithms of the state-of-the art of the Machine Learning Systems. 1
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."
Abstract
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Cited by 578 (16 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning
Combining labeled and unlabeled data with co-training
, 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
Abstract
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Cited by 1633 (28 self)
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We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated
Object recognition with partially labeled examples
- Master’s thesis, Massachusetts Inst. of Technology
, 2002
"... The Problem: Learning to recognize objects from very few labeled training examples, but large numbers of unlabeled examples. Motivation: Statistical object recognition techniques require large training sets to achieve good performance. It is often difficult and expensive to collect many labeled exam ..."
Abstract
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Cited by 2 (0 self)
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The Problem: Learning to recognize objects from very few labeled training examples, but large numbers of unlabeled examples. Motivation: Statistical object recognition techniques require large training sets to achieve good performance. It is often difficult and expensive to collect many labeled
Probability: Theory and examples
- CAMBRIDGE U PRESS
, 2011
"... Some times the lights are shining on me. Other times I can barely see. Lately it occurs to me what a long strange trip its been. Grateful Dead In 1989 when the first edition of the book was completed, my sons David and Greg were 3 and 1, and the cover picture showed the Dow Jones at 2650. The last t ..."
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Cited by 1331 (16 self)
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twenty years have brought many changes but the song remains the same. The title of the book indicates that as we develop the theory, we will focus our attention on examples. Hoping that the book would be a useful reference for people who apply probability in their work, we have tried to emphasize
Object Recognition with Partially Labeled Examples
- Master’s thesis, Massachusetts Inst. of Technology
, 2002
"... Machine learning algorithms tend to improve in performance with larger training sets, but obtaining a large amount of training data comes at a high cost. Several methods of semi-supervised learning have been introduced recently to take advantage of a larger training set without the burden of labeli ..."
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of labeling many samples. We apply these semisupervised learning methods to a data set of cars and background images, attempting to separate the two classes. Some of the algorithms obtain very high classification accuracy and can be used towards a car-detection system.
SEMI-SUPERVISED LEARNING WITH PARTIALLY LABELED EXAMPLES
, 2010
"... Traditionally, machine learning community has been focused on supervised learn-ing where the source of learning is fully labeled examples including both input features and corresponding output labels. As one way to alleviate the costly effort of collecting fully labeled examples, semi-supervised lea ..."
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Traditionally, machine learning community has been focused on supervised learn-ing where the source of learning is fully labeled examples including both input features and corresponding output labels. As one way to alleviate the costly effort of collecting fully labeled examples, semi
Unsupervised Models for Named Entity Classification
- In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
, 1999
"... This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabe ..."
Abstract
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Cited by 542 (4 self)
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This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use
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