نتایج جستجو برای: iterative rule learning

تعداد نتایج: 791317  

Journal: :Fuzzy Sets and Systems 2004
Frank Hoffmann

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...

Journal: :Fuzzy Sets and Systems 1998
Francisco Herrera Manuel Lozano José L. Verdegay

The purpose of this paper is to present a genetic learning process for learning fuzzy control roles from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuz...

2001
Frank Hoffmann

This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

Journal: :Neurocomputing 2007
Zhirong Yang Jorma Laaksonen

We present here how to construct multiplicative update rules for non-negative projections based on Oja’s iterative learning rule. Our method integrates the multiplicative normalization factor into the original additive update rule as an additional term which generally has a roughly opposite direction. As a consequence, the modified additive learning rule can easily be converted to its multiplic...

2005
Michelle Galea Qiang Shen Vishal Singh

Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Between SPBA runs, cases in the training set that are covered by the newly evolved rule are generally removed...

2010
Stephen G. Matthews Mario A. Góngora Adrian A. Hopgood

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to si...

Journal: :AMIA ... Annual Symposium proceedings. AMIA Symposium 2005
Lewis J. Frey Mary E. Edgerton Douglas H. Fisher Lianhong Tang Zhihua Chen

An iterative computational scientific discovery approach is proposed and applied to gene expression data for resectable lung adenocarcinoma patients. We use genes learned from the C5.0 rule induction algorithm, clinical features and prior knowledge derived from a network of interacting genes as represented in a database obtained with PathwayAssist to discover markers for prognosis in the gene e...

2010
Yuehua Xu Alan Fern Sung Wook Yoon

Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection...

Journal: :Soft Comput. 2009
Luciano Sánchez José Otero Inés Couso

Backfitting of fuzzy rules is an Iterative Rule Learning technique for obtaining the knowledge base of a fuzzy rule-based system in regression problems. It consists in fitting one fuzzy rule to the data, and replacing the whole training set by the residual of the approximation. The obtained rule is added to the knowledge base, and the process is repeated until the residual is zero, or near zero...

2009
Edwin Lughofer Stefan Kindermann

In this paper, we are dealing with a novel data-driven learning method (SparseFIS) for Takagi-Sugeno fuzzy systems, extended by including rule weights. Our learning method consists of three phases: the first phase conducts a clustering process in the input/output feature space with iterative vector quantization. Hereby, the number of clusters = rules is pre-defined and denotes a kind of upper b...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید