Above are papers related to the following paper: [Paper_ID] Paper #143476. 	[Title] EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING [Abstract] The work discussed in this paper is motivated by the need of building decision support systems for real-world problem domains. Our goal is to use these systems as a tool for supporting Bayes optimal decision making, where the action maximizing the expected utility, with respect to predicted probabilities of the possible outcomes, should be selected. For this reason, the models used need to be probabilistic in nature | the output of a model has to be a probability distribution, not just a set of numbers. For the model family, we have chosen the set of simple discrete finite mixture models which have the advantage of being computationally very efficient. In this work, we describe a Bayesian approach for constructing finite mixture models from sample data. Our approach is based on a two-phase unsupervised learning process which can be used both for exploratory analysis and model construction. In the first phase, the selection of a model class, i.e., the number of parameters, is performed by calculating the Cheeseman-Stutz approximation for the model class evidence. In the second phase, the MAP parameters in the selected class are estimated by the EM algorithm. In this framework, the overfitting problem common to many traditional learning approaches can be avoided, as the learning process automatically regulates the complexity of the model. This paper focuses on the model class selection phase and the approach is validated by presenting empirical results with both natural and synthetic data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18777. 	[Title] A Tutorial on Learning With Bayesian Networks [Abstract] Technical Report MSR-TR-95-06 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18811. 	[Title] Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures [Abstract] We investigate the problem of computing the posterior probability of a model class, given a data sample and a prior distribution for possible parameter settings. By a model class we mean a group of models which all share the same parametric form. In general this posterior may be very hard to compute for high-dimensional parameter spaces, which is usually the case with real-world applications. In the literature several methods for computing the posterior approximately have been proposed, but the quality of the approximations may depend heavily on the size of the available data sample. In this work we are interested in testing how well the approximative methods perform in real-world problem domains. In order to conduct such a study, we have chosen the model family of finite mixture distributions. With certain assumptions, we are able to derive the model class posterior analytically for this model family. We report a series of model class selection experiments on real-world data sets, where the true posterior and the approximations are compared. The empirical results support the hypothesis that the approximative techniques can provide good estimates of the true posterior, especially when the sample size grows large. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112686. 	[Title] Bayesian Finite Mixtures for Nonlinear Modeling of Educational data [Abstract] In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a data set from a recent educational study. The Bayesian classification approach described has been implemented, and presents an appealing addition to the standard toolbox for exploratory data analysis of educational data. Please summarise all the given information.

Paper #143476 has the title `EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING  
` and the abstract `The work discussed in this paper is motivated by the need of building decision support systems for real-world problem domains. Our goal is to use these systems as a tool for supporting Bayes optimal decision making, where the action maximizing the expected utility, with respect to predicted probabilities of the possible outcomes, should be selected. For this reason, the models used need to be probabilistic in nature | the output of a model has to be a probability distribution, not just a set of numbers. For the model family, we have chosen the set of simple discrete finite mixture models which have the advantage of being computationally very efficient. In this work, we describe a Bayesian approach for constructing finite mixture models from sample data. Our approach is based on a two-phase unsupervised learning process which can be used both for exploratory analysis and model construction. In the first phase, the selection of a model class, i.e., the number of parameters, is performed by calculating the Cheeseman-Stutz approximation for the model class evidence. In the second phase, the MAP parameters in the selected class are estimated by the EM algorithm. In this framework, the overfitting problem common to many traditional learning approaches can be avoided, as the learning process automatically regulates the complexity of the model. This paper focuses on the model class selection phase and the approach is validated by presenting empirical results with both natural and synthetic data. 
`.
Question: Which category should Paper #143476 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #143476 has the title `EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING  
` and the abstract `The work discussed in this paper is motivated by the need of building decision support systems for real-world problem domains. Our goal is to use these systems as a tool for supporting Bayes optimal decision making, where the action maximizing the expected utility, with respect to predicted probabilities of the possible outcomes, should be selected. For this reason, the models used need to be probabilistic in nature | the output of a model has to be a probability distribution, not just a set of numbers. For the model family, we have chosen the set of simple discrete finite mixture models which have the advantage of being computationally very efficient. In this work, we describe a Bayesian approach for constructing finite mixture models from sample data. Our approach is based on a two-phase unsupervised learning process which can be used both for exploratory analysis and model construction. In the first phase, the selection of a model class, i.e., the number of parameters, is performed by calculating the Cheeseman-Stutz approximation for the model class evidence. In the second phase, the MAP parameters in the selected class are estimated by the EM algorithm. In this framework, the overfitting problem common to many traditional learning approaches can be avoided, as the learning process automatically regulates the complexity of the model. This paper focuses on the model class selection phase and the approach is validated by presenting empirical results with both natural and synthetic data. 
`.
Question: Which category should Paper #143476 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: Probabilistic_Methods