doi: 10.4156/jcit.vol5.issue8.18 To solve the problem of the noise existed in feature items of category template in filtering system, weight adjusting strategy based on average fitness of population is proposed combining genetic algorithm with feedback. The feature items ’ contribution to individual fitness is studied to adjust feature items ’ weight by the genetic difference in the average fitness of the individual. Experimental results show that the weight adjusting strategy could significantly optimize filtering effect
Feature manipulation refers to the process by which the input space of a machine learning task is al...
This paper describes an application of the Learnable Evolution Model (LEM) to a digital signal filte...
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness functi...
Genetic algorithms have been created as an optimization strategy to be used especially when complex ...
Adaptive Information Filtering seeks a solution to the problem of information overload through a tai...
A feature weighting and selection method is proposed which uses the structure of a weightless neuron...
Most of the techniques used in text classification are determined by the occurrences of the words (...
In pattern classification, feature selection is an important factor in the performance of classi-fie...
Feature selection is an important part of machine learning and data mining which may enhance the spe...
The Mel-Frequency Cepstral Coefficients (MFCC) and their derivatives are commonly used as acoustic f...
We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the ...
The Mel-Frequency Cepstral Coefficients (MFCC) are widely accepted as a suitable representation for...
One of the major challenges in automatic classification is to deal with highly dimensional data. Sev...
Variational methods for image denoising consist of minimizing a functional which incorporates both t...
In this paper we develope a new method based on the genetic algorithm to solve the total variation-b...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
This paper describes an application of the Learnable Evolution Model (LEM) to a digital signal filte...
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness functi...
Genetic algorithms have been created as an optimization strategy to be used especially when complex ...
Adaptive Information Filtering seeks a solution to the problem of information overload through a tai...
A feature weighting and selection method is proposed which uses the structure of a weightless neuron...
Most of the techniques used in text classification are determined by the occurrences of the words (...
In pattern classification, feature selection is an important factor in the performance of classi-fie...
Feature selection is an important part of machine learning and data mining which may enhance the spe...
The Mel-Frequency Cepstral Coefficients (MFCC) and their derivatives are commonly used as acoustic f...
We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the ...
The Mel-Frequency Cepstral Coefficients (MFCC) are widely accepted as a suitable representation for...
One of the major challenges in automatic classification is to deal with highly dimensional data. Sev...
Variational methods for image denoising consist of minimizing a functional which incorporates both t...
In this paper we develope a new method based on the genetic algorithm to solve the total variation-b...
Feature manipulation refers to the process by which the input space of a machine learning task is al...
This paper describes an application of the Learnable Evolution Model (LEM) to a digital signal filte...
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness functi...