The development of parallel batch pattern back propagation training algorithm of multilayer perceptron and its scalability research on general-purpose parallel computer are presented in this paper. The model of multilayer perceptron and batch pattern training algorithm are theoretically described. The algorithmic description of the parallel batch pattern training method is presented. The scalab...

The traditional KNN text classification algorithm used all training samples for classification, so it had a huge number of training samples and a high degree of calculation complexity, and it also didn’t reflect the different importance of different samples. In allusion to the problems mentioned above, an improved KNN text classification algorithm based on clustering center is proposed in this ...

We propose a training symbol based channel estimation (TS-EST) algorithm that estimates the 2 × 2 Jones channel matrix. The estimated matrix entries are then used as the initial center taps of the 2 × 2 butterfly equalizer. Employing very few training symbols for TS-EST, ultrafast polarization tracking is achieved and tap update can be initially pursued using the decision-directed least mean sq...

In this paper we analyze the average behavior of the Bayes-optimal and Gibbs learning algorithms. We do this both for oo-training-set error and conventional IID error (for which test sets overlap with training sets). For the IID case we provide a major extension to one of the better known results of 7]. We also show that expected IID test set error is a non-increasing function of training set s...

In this paper we analyze the average behavior of the Bayes-optimal and Gibbs learning algorithms. We do this both for oo-training-set error and conventional IID error (for which test sets overlap with training sets). For the IID case we provide a major extension to one of the better known results of 7]. We also show that expected IID test set error is a non-increasing function of training set s...

The Coulomb Energy network offers a unique perspective towards nonlinear transformations. However, its training as it was originally proposed by C. Scofield [1] presented difficulties that prevented its general use. We have investigated this model and we present here the reasons for its shortcomings. Further we propose refinements to the model and its training algorithm, and we present the stud...

Generalized Discriminative Feature Transformation (GDFT) is a feature space discriminative training algorithm for automatic speech recognition (ASR). GDFT uses Lagrange relaxation to transform the constrained maximum likelihood linear regression (CMLLR) algorithm for feature space discriminative training. This paper presents recent improvements on GDFT, which are achieved by regularization to t...

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausi...

the present article investigates the application of high order tsk (takagi sugeno kang) fuzzy systems in modeling photo voltaic (pv) cell characteristics. a method has been introduced for training second order tsk fuzzy systems using anfis (artificial neural fuzzy inference system) training method. it is clear that higher order tsk fuzzy systems are more precise approximators while they cover n...

Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vecto...