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Bayesian Network Classiers in Weka
, 2005
"... Various Bayesian network classier learning algorithms are implemented in Weka [10]. This note provides some user documentation and implementation details. Summary of main capabilities: Structure learning of Bayesian networks using various hill climbing (K2, B, etc) and general purpose (simulated a ..."
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Various Bayesian network classier learning algorithms are implemented in Weka [10]. This note provides some user documentation and implementation details. Summary of main capabilities: Structure learning of Bayesian networks using various hill climbing (K2, B, etc) and general purpose (simulated
2.2 Classiers Used............................ 5
, 2006
"... Success of optical character recognition depends on a number of factors, two of which are feature extraction and classication algorithms. In this paper we look at the results of the application of a set of classiers to datasets obtained ..."
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Success of optical character recognition depends on a number of factors, two of which are feature extraction and classication algorithms. In this paper we look at the results of the application of a set of classiers to datasets obtained
Learning Polyhedral Classiers Using Logistic Function
"... In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based m ..."
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model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified
Using Bayesian Networks for Selecting Classiers in GP Ensembles
"... Ensemble techniques have been widely used to improve classication performance also in the case of GPbased systems. These techniques should improve classication accuracy by using voting strategies to combine the responses of different classi ers. However, even reducing the number of classiers compos ..."
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Ensemble techniques have been widely used to improve classication performance also in the case of GPbased systems. These techniques should improve classication accuracy by using voting strategies to combine the responses of different classi ers. However, even reducing the number of classiers
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 231 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
A Support Vector Machine Classier for Gene Name Recognition
"... This summary describes our solution for task 1A of the BioCreAtIvE Challenge Cup 2003. Essentially, we reduce the entity recognition problem to the problem of classifying single words using a Support Vector Machine followed by a term expansion. Our research question is therefore to nd those types o ..."
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This summary describes our solution for task 1A of the BioCreAtIvE Challenge Cup 2003. Essentially, we reduce the entity recognition problem to the problem of classifying single words using a Support Vector Machine followed by a term expansion. Our research question is therefore to nd those types
Bayesian Classiers are Large Margin Hyperplanes in a Hilbert Space
 Machine Learning: Proceedings of the Fifteenth International Conference
, 1998
"... Bayesian algorithms for Neural Networks are known to produce classiers which are very resistent to overtting. It is often claimed that one of the main distinctive features of Bayesian Learning Algorithms is that they don't simply output one hypothesis, but rather an entire distribution of pro ..."
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Cited by 1 (1 self)
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Bayesian algorithms for Neural Networks are known to produce classiers which are very resistent to overtting. It is often claimed that one of the main distinctive features of Bayesian Learning Algorithms is that they don't simply output one hypothesis, but rather an entire distribution
Calibrating Recurrent Sliding Window Classiers for Sequential Supervised Learning
"... Sequential supervised learning problems involve assigning a class label to each item in a sequence Examples include partofspeech tagging and texttospeech mapping A very generalpurpose strat egy for solving such problems is to construct a recurrent sliding window RSW classier which maps some windo ..."
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Sequential supervised learning problems involve assigning a class label to each item in a sequence Examples include partofspeech tagging and texttospeech mapping A very generalpurpose strat egy for solving such problems is to construct a recurrent sliding window RSW classier which maps some
Learning Pattern Tree Classiers Using a CoEvolutionary Algorithm
"... Pattern tree induction has recently been introduced as a novel method for classication. Roughly speaking, a pattern tree is a hierarchical, treelike structure, whose inner nodes are marked with generalized (fuzzy) logical operators, and a pattern tree classier consists of one such tree per class ..."
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Pattern tree induction has recently been introduced as a novel method for classication. Roughly speaking, a pattern tree is a hierarchical, treelike structure, whose inner nodes are marked with generalized (fuzzy) logical operators, and a pattern tree classier consists of one such tree per
Designing Nearest Neighbour Classiers by the Evolution of a Population of Prototypes
"... Abstract. A new evolutionary algorithm to design nearest neightbour classi ers is presented in this paper. Main design topics of this sort of classi ers are the number of prototypes used and their position. This algorithm is based on the evolution of a population of prototypes that try to achieve an ..."
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Abstract. A new evolutionary algorithm to design nearest neightbour classi ers is presented in this paper. Main design topics of this sort of classi ers are the number of prototypes used and their position. This algorithm is based on the evolution of a population of prototypes that try to achieve an equilibrium by nding the right size of the population and the position of each prototype in the environment, solving at the same time both design topics above. A biological point of view is given to explain most of the concepts introduced, as well as the operators used in evolution.
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