Above are papers related to the following paper: [Paper_ID] Paper #116021. 	[Title] Improving Generalization with Active Learning [Abstract] Active learning differs from passive "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 that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective sampling, and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SG-network, on three domains, and observe significant improvement in generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12960. 	[Title] Active Learning with Committees for Text Categorization [Abstract] In many real-world domains like text categorization, supervised learning requires a large number of training examples. In this paper we describe an active learning method that uses a committee of learners to reduce the number of training examples required for learning. Our approach is similar to the Query by Committee framework, where disagreement among the committee members on the predicted label for the input part of the example is used to signal the need for knowing the actual value of the label. Our experiments in text categorization using a committee of Winnow-based learners demonstrate that this approach can reduce the number of labeled training examples required over that used by a single Winnow learner by 1-2 orders of magnitude. This paper is not under review or accepted for publication in another conference or journal. Acknowledgements: The availability of the Reuters-22173 corpus [Reuters] and of the | STAT Data Manipulation and Analysis Programs [Perlman] has greatly assisted in our research to date. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #105899. 	[Title] In:  A Mixture Model System for Medical and Machine Diagnosis [Abstract] Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27627. 	[Title] Information-based objective functions for active data selection [Abstract] Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686061. 	[Title] An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks. [Abstract] Macquarie University Technical Report No. 95-173C Department of Computing School of MPCE, Macquarie University, New South Wales, Australia Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686030. 	[Title] Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons [Abstract] We propose an active learning method with hidden-unit reduction, which is devised specially for multilayer perceptrons (MLP). First, we review our active learning method, and point out that many Fisher-information-based methods applied to MLP have a critical problem: the information matrix may be singular. To solve this problem, we derive the singularity condition of an information matrix, and propose an active learning technique that is applicable to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23116. 	[Title] Bayesian Methods for Adaptive Models [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27632. 	[Title] REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS [Abstract] For an agent living in a non-deterministic Markov environment (NME), what is, in theory, the fastest way of acquiring information about its statistical properties? The answer is: to design "optimal" sequences of "experiments" by performing action sequences that maximize expected information gain. This notion is implemented by combining concepts from information theory and reinforcement learning. Experiments show that the resulting method, reinforcement driven information acquisition, can explore certain NMEs much faster than conventional random exploration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33325. 	[Title] Applying Winnow to Context-Sensitive Spelling Correction [Abstract] Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classifiers. We find that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19231. 	[Title] Query by Committee [Abstract] We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30934. 	[Title] Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee [Abstract] We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of perceptrons. Keywords: selective sampling, query learning, Bayesian Learning, experimental design fl Yoav Freund, Room 2B-428, AT&T Laboratories, 700 Mountain Ave., Murray Hill, NJ, 07974. Telephone:908-582-3164. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28254. 	[Title] In  Active Learning with Statistical Models [Abstract] For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regres sion are both efficient and accurate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686015. 	[Title] "What is the best thing to do right now?": getting beyond greedy exploration [Abstract]  Please summarise all the given information.

Paper #116021 has the title `Improving Generalization with Active Learning  
` and the abstract `Active learning differs from passive "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 that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective sampling, and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SG-network, on three domains, and observe significant improvement in generalization.
`.
Question: Which category should Paper #116021 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #116021 has the title `Improving Generalization with Active Learning  
` and the abstract `Active learning differs from passive "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 that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective sampling, and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SG-network, on three domains, and observe significant improvement in generalization.
`.
Question: Which category should Paper #116021 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory