Short-term cepstral features have long been chosen as standard features for speaker recognition thanks to their relevance and effectiveness. In contrast, discriminative features, calculated by a multi-layer perceptron (MLP) from much longer stretches of time, have been gradually adopted in automatic speech recognition (ASR). It has been shown that augmenting short-term cepstral features with lo...

In now a days Password authentication plays a vital role typing biometrics as a transparent layer of user authentication for security. Keystroke authentication provides security to the applications like electronic mail, banking and any other web services etc., There are several types of techniques to protect our passwords but it can be easily hacked by the hackers through so many algorithms. By...

Within forest growth modeling LOGIT models are used to predict individual tree mortality. In this paper we present, Multi-Layer Perceptron, Learning Vector Quantization and Cascade Correlation networks as different formalisms for mortality predictions. The data set for parameterizing the LOGIT model and training the different neural network types comes from the Austrian National Forest Inventor...

In this paper we describe a hybrid intelligent system for classification of cardiac arrhythmias. The hybrid approach was tested with the ECG records of the MIT-BIH Arrhythmia Database. The samples considered for classification contained arrhythmias of the following types: LBBB, RBBB, PVC and Fusion Paced and Normal, as well as the normal heartbeats. The signals of the arrhythmias were segmented...

Five classifiers including the K-means, Fuzzy c-means, K-nearest neighbour, Multi-Layer Perceptron Neural Network and Probabilistic Neural Network classifiers are compared for application to colour grade classification and detection of bruising of Granny Smith apples. A number of suitable discriminate features are determined heuristically for the categorisation of four classes including: high g...

Adaptative training of the non-linear single-layer perceptron can lead to the Euclidean distance classifier and later to the standard Fisher linear discriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the “weight decay” regularization term added to the cost function. Thus early stopping plays a role of regularization ...

Artificial Neural Network (ANN) is one of the modern computational methods proposed to solve increasingly complex problems in the real world (Xie et al., 2006 and Chau, 2007). ANN is characterized by its pattern of connections between the neurons (called its architecture), its method of determining the weights on the connections (called its training, or learning, algorithm), and its activation ...

| We investigate an energy function for MLP called superposed energy. Applying to autoassociative learning of a sandglass-type MLP, it can adaptively adjust the e ective number of the bottlenecklayer units to the intrinsic dimensionality of nonlinear data, and the optimal dimensionality reduced representation can be extracted after learning.

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the pres...

The Multi-Layer Perceptron (MLP) is one of the most widely applied and researched Artificial Neural Network model. MLP networks are normally applied to performing supervised learning tasks, which involve iterative training methods to adjust the connection weights within the network. This is commonly formulated as a multivariate non-linear optimization problem over a very high-dimensional space ...