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679
Locally Weighted Learning for Control
, 1996
"... Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We ex ..."
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Cited by 194 (18 self)
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Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control.
Learning Deep Architectures for AI
"... Theoretical results suggest that in order to learn the kind of complicated functions that can represent highlevel abstractions (e.g. in vision, language, and other AIlevel tasks), one may need deep architectures. Deep architectures are composed of multiple levels of nonlinear operations, such as i ..."
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Cited by 183 (30 self)
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Theoretical results suggest that in order to learn the kind of complicated functions that can represent highlevel abstractions (e.g. in vision, language, and other AIlevel tasks), one may need deep architectures. Deep architectures are composed of multiple levels of nonlinear operations, such as in neural nets with many hidden layers or in complicated propositional formulae reusing many subformulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the stateoftheart in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of singlelayer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Neural network exploration using optimal experiment design
 Neural Networks
, 1994
"... We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network lear ..."
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Cited by 161 (2 self)
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We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely.We conclude that, while not a panacea, OEDbased query/action has muchto offer, especially in domains where its high computational costs can be tolerated.
Curriculum Learning
"... Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them “curriculum ..."
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Cited by 147 (15 self)
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Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them “curriculum learning”. In the context of recent research studying the difficulty of training in the presence of nonconvex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various setups. The experiments show that significant improvements in generalization can be achieved. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of nonconvex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of nonconvex functions). 1.
CommitteeBased Sampling For Training Probabilistic Classifiers
 In Proceedings of the Twelfth International Conference on Machine Learning
, 1995
"... In many realworld learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper proposes a general method for efficiently training probabilistic classifiers, by selecting for training only the more informative examples in a stream of unlabeled examples. ..."
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Cited by 145 (3 self)
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In many realworld learning tasks, it is expensive to acquire a sufficient number of labeled examples for training. This paper proposes a general method for efficiently training probabilistic classifiers, by selecting for training only the more informative examples in a stream of unlabeled examples. The method, committeebased sampling, evaluates the informativeness of an example by measuring the degree of disagreement between several model variants. These variants (the committee) are drawn randomly from a probability distribution conditioned by the training set selected so far (MonteCarlo sampling). The method is particularly attractive because it evaluates the expected information gain from a training example implicitly, making the model both easy to implement and generally applicable. We further show how to apply committeebased sampling for training Hidden Markov Model classifiers, which are commonly used for complex classification tasks. The method was implemented and tested for ...
Active SemiSupervision for Pairwise Constrained Clustering
 Proc. 4th SIAM Intl. Conf. on Data Mining (SDM2004
"... Semisupervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of mustlink and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for acti ..."
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Cited by 136 (9 self)
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Semisupervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of mustlink and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision. 1
Types of cost in inductive concept learning
 In Workshop on CostSensitive Learning at the Seventeenth International Conference on Machine Learning
, 2000
"... Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted ..."
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Cited by 132 (0 self)
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Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In realworld applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on costsensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth. 1.
Semisupervised Clustering with User Feedback
, 2003
"... We present a new approach to clustering based on the observation that \it is easier to criticize than to construct." Our approach of semisupervised clustering allows a user to iteratively provide feedback to a clustering algorithm. The feedback is incorporated in the form of constraints w ..."
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Cited by 125 (2 self)
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We present a new approach to clustering based on the observation that \it is easier to criticize than to construct." Our approach of semisupervised clustering allows a user to iteratively provide feedback to a clustering algorithm. The feedback is incorporated in the form of constraints which the clustering algorithm attempts to satisfy on future iterations. These constraints allow the user to guide the clusterer towards clusterings of the data that the user nds more useful. We demonstrate semisupervised clustering with a system that learns to cluster news stories from a Reuters data set. Introduction Consider the following problem: you are given 100,000 text documents (e.g., papers, newsgroup articles, or web pages) and asked to group them into classes or into a hierarchy such that related documents are grouped together. You are not told what classes or hierarchy to use or what documents are related; you have some criteria in mind, but may not be able to say exactly w...
Covariate shift adaptation by importance weighted cross validation
, 2000
"... A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapo ..."
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Cited by 122 (55 self)
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A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift. Under the covariate shift, standard model selection techniques such as cross validation do not work as desired since its unbiasedness is no longer maintained. In this paper, we propose a new method called importance weighted cross validation (IWCV), for which we prove its unbiasedness even under the covariate shift. The IWCV procedure is the only one that can be applied for unbiased classification under covariate shift, whereas alternatives to IWCV exist for regression. The usefulness of our proposed method is illustrated by simulations, and furthermore demonstrated in the braincomputer interface, where strong nonstationarity effects can be seen between training and test sessions. c2000 Masashi Sugiyama, Matthias Krauledat, and KlausRobert Müller.
Combining Active Learning and SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 ICML 2003 workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining
, 2003
"... Active and semisupervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semisupervi ..."
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Cited by 121 (5 self)
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Active and semisupervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semisupervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection. 1.