~ Recently, a minimum classification error training algorithm has been proposed for minimizing the misclassification probability based on a given set of training samples using a generalized probabilistic descent method. This algorithm is a type of discriminative learning algorithm, but it approaches the objective of minimum classification error in a more direct manner than the conventional disc...

This paper proposes Centroid Neural Network with Directional Spawning for efficient data clustering. The proposed algorithm locates each new neuron for cluster center by considering the location trajectory of previously added neurons during training process. Experiments on different data sets are performed in order to evaluate the performance of training error, training speed, and test error. T...

In this paper, we first introduce some facts about semi-supervised learning and its often used methods such as generative mixture models, self-training, co-training and Transductive SVM and so on. Then we present a self-training semi-supervised SVM algorithm based on which we give out a modified algorithm. In order to demonstrate its validity and effectiveness, we carry out some experiments whi...

This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan’s nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially. Although the basic tree-building algorithms differ only in how the decision...

in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

estimate of sediment load is required in a wide spectrum of water resources engineering problems. the nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. in this study artificial neural networks (anns) are employed to estimate daily suspended sediment load. two different ann algorithms, multi layer percept...

Dimensionality reduction is an important problem in pattern recognition. Reducing the dimensionality of feature can improve the effecitveness and efficiency of pattern recognition algorithms. Minimum Classification Error(MCE) training algorithm is a power tool for dimensionality resuction. However, MCE training process is a type of thorough search process for the local minimum, global minimum c...

For prediction of future class label or Variable value, there are two techniques respectively – Classification and Regression [2,5]. Data classification is a process between query point and training dataset to categorize query point. Using pre-labeled training dataset and classification algorithm, class label is assigned to new query point. Pre-labeled training dataset is taken from historical ...

According to the structure of the BP neural network and the algorithm, choose three methods of BP neural network algorithm was improved, through analysis and comparison, computing speed is faster, more accurate judgment Levenberg Marquardt algorithm as the improved algorithm of optimal; Using the algorithm to the established BP neural network for training analysis; Then use the Matlab software,...

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are di...