Training the Multilayer Perceptron Classifier with a Two-Stage Method
نویسنده
چکیده
We propose a two-stage training for the multilayer perceptron (MLP). The first stage is bottom-up, where we use a class separability measure to conduct hidden layer training and the least squared error criterion to train the output layer. The second stage is top-down, we use a criterion derived from classification error rate to further train the network weights. We demonstrate the effectiveness of the proposed training algorithms with computer simulations.
منابع مشابه
A Pattern Classifier Integrating Multilayer Perceptron and Error-Correcting Code
In this paper we present a novel classifier which integrates a multilayer perceptron and a error-correcting decoder. There are two stages in the classifier, in the first stage, mapping feature vectors from feature space to code space is achieved by a multilayer perceptron; in the second stage, error correcting decoding is done on code space, by which the index of the noisy codeword can be obtai...
متن کاملArray of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classification task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training sa...
متن کاملFace recognition based on perceived facial images and multilayer perceptron neural network using constructive training algorithm
This study presents a modified constructive training algorithm for multilayer perceptron (MLP) which is applied to face recognition problem. An incremental training procedure has been employed where the training patterns are learned incrementally. This algorithm starts with a small number of training patterns and a single hidden-layer using an initial number of neurons. During the training, the...
متن کاملPoly-co: a multilayer perceptron approach for coreference detection
This paper presents the coreference resolution system Poly-co submitted to the closed track of the CoNLL-2011 Shared Task. Our system integrates a multilayer perceptron classifier in a pipeline approach. We describe the heuristic used to select the pairs of coreference candidates that are feeded to the network for training, and our feature selection method. The features used in our approach are...
متن کاملHyperplane Training of a Hypersphere Classifier
A novel classifier architecture is introduced which belongs to both hyperplane and hypersphere families. The basic computational unit in the architecture is a perceptron whose input is augmented by its squared length. Traditional methods of training hyperplane classifiers (perceptron training algorithm, backpropagation, etc.) function in the augmented input space, and induce hyperspherical deci...
متن کامل