Association and classification are two important tasks in data analysis, machine learning, data mining and knowledge discovery. Intensive studies have been carried out in these areas recently, but how to apply discove...
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Association and classification are two important tasks in data analysis, machine learning, data mining and knowledge discovery. Intensive studies have been carried out in these areas recently, but how to apply discovered event associations to classification is still seldom found in current publications. We first introduce a method based on residual analysis to discover statistically significant event associations from a database. Then we propose a measure (weight of evidence) to evaluate the evidence of a significant event association in support of, or against, a certain class membership. This measure can be applied to classify an observation with respect to any attribute. With this approach, we achieve flexible prediction. Empirical results on different data sets are discussed.
The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and actions of multiple individuals must be inferred from noisy and am...
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The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and actions of multiple individuals must be inferred from noisy and ambiguous data. We argue that Bayesian network models are an attractive statistical framework for cue fusion in these applications. Bayes nets combine a natural mechanism for expressing contextual information with efficient algorithms for learning and inference. We illustrate these points through the development of a Bayes net model for detecting when a user is speaking. The model combines four simple vision sensors: face detection, skin color, skin texture, and mouth motion. We present some promising experimental results.
Cooperation by voting is one of the popular modular neural network decision-making strategies. Ensemble classifiers are multiple identical modules which use voting for post-learning classification. This paper suggests...
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Cooperation by voting is one of the popular modular neural network decision-making strategies. Ensemble classifiers are multiple identical modules which use voting for post-learning classification. This paper suggests a new cooperation scheme for ensembles which utilizes voting in the learning process itself. According to the suggested scheme, different modules would, automatically, focus on different regions in the input space. Hence, temporal crosstalk decreases and decision boundaries are drawn accurately in complex overlapping regions of the input space.
Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficien...
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ISBN:
(纸本)0818688211
Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficient algorithms for statistical estimation of such dynamic systems is very difficult. Conventional linear least squares methods are impractical for both computational and storage reasons. A previously-developed multiscale estimation methodology has been successfully applied to a number of large-scale static estimation problems. In this paper we apply the multiscale approach to the more challenging dynamic estimation problems, introducing a recursive procedure that efficiently propagates multiscale models for the estimation errors in a manner analogous to, but more efficient than, the Kalman filter's propagation of the error covariances. We illustrate our research in the context of 1-D and 2-D diffusive processes.
The current generation of non-modular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks'...
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ISBN:
(纸本)0780341236
The current generation of non-modular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks' hidden nodes. We propose the Cooperative Modular Neural Network (CMNN) architecture, which deals with different levels of overlap in different modules. The modules share their information and cooperate in taking a global classification decision through voting. Moreover, special modules are dedicated to resolve high overlaps in the input-space. The performance of the new model outperforms that of the nonmodular alternative when when applied to ten famous benchmark classification problems.
The current generation of nonmodular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks'...
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The current generation of nonmodular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks' hidden nodes. We propose the Cooperative Modular Neural Network (CMNN) architecture, which deals with different levels of overlap in different modules. The modules share their information and cooperate in taking a global classification decision through voting. Moreover, special modules are dedicated to resolve high overlaps in the input-space. The performance of the new model outperforms that of the nonmodular alternative when when applied to ten famous benchmark classification problems.
作者：
Lopez-Suarez, AlexKamel, M.Lab
Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada
This paper presents a new methodology to restructure rule bases through the combination of Explanation-Based Learning (EBL) and knowledge abstraction techniques. Performance improvements resulting from restructuring a...
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