Abstract—A two-stage linear-in-the-parameter model construc-tion algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model’s generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is em-ployed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassi-fication rate. It is shown that t...
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. Th...
Consider a two-class classification problem where the number of features is much larger than the sam...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-clas...
A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-clas...
A novel two-stage construction algorithm for linear-in-the-parameters classifiers is proposed, aimin...
In this paper we propose an efficient two-level model identification method for a large class of lin...
An efficient two-level model identification method aiming at maximising a model׳s generalisation cap...
We propose a simple and computationally efficient construction algorithm for two class linear-in-the...
Many kernel classifier construction algorithms adopt classification accuracy as performance metrics ...
Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classifi...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
We present results about classes of prefilters that may result in similar model estimates by use of ...
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. Th...
Consider a two-class classification problem where the number of features is much larger than the sam...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-clas...
A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-clas...
A novel two-stage construction algorithm for linear-in-the-parameters classifiers is proposed, aimin...
In this paper we propose an efficient two-level model identification method for a large class of lin...
An efficient two-level model identification method aiming at maximising a model׳s generalisation cap...
We propose a simple and computationally efficient construction algorithm for two class linear-in-the...
Many kernel classifier construction algorithms adopt classification accuracy as performance metrics ...
Elastic Net Regularizers have shown much promise in designing sparse classifiers for linear classifi...
An efficient model identification algorithm for a large class of linear-in-the-parameters models is ...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
In pattern recognition and machine learning, a classification problem refers to finding an algorithm...
We present results about classes of prefilters that may result in similar model estimates by use of ...
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. Th...
Consider a two-class classification problem where the number of features is much larger than the sam...
In the traditional system identification techniques, a priori model structure is widely assumed to b...