Discriminative motif optimization based on perceptron training

Discriminative motif optimization based on perceptron training

MOTIVATION Generating accurate transcription factor (TF) binding site motifs from data generated using the next-generation sequencing, especially ChIP-seq, is challenging. The challenge arises because a typical experiment reports a large number of sequences bound by a TF, and the length of each sequence is relatively long. Most traditional motif finders are slow in handling such enormous amount...

Optimization on decoding graphs by discriminative training

The three main knowledge sources used in the automatic speech recognition (ASR), namely the acoustic models, a dictionary and a language model, are usually designed and optimized in isolation. Our previous work [1] proposed a methodology for jointly tuning these parameters, based on the integration of the resources as a finite-state graph, whose transition weights are trained discriminatively. ...

Multilayer Perceptron: Architecture Optimization and Training

Improving Particle Swarm Optimization Based on Neighborhood and Historical Memory for Training Multi-Layer Perceptron

Many optimization problems can be found in scientific and engineering fields. It is a challenge for researchers to design efficient algorithms to solve these optimization problems. The Particle swarm optimization (PSO) algorithm, which is inspired by the social behavior of bird flocks, is a global stochastic method. However, a monotonic and static learning model, which is applied for all partic...

Minimum divergence based discriminative training

We propose to use Minimum Divergence(MD) as a new measure of errors in discriminative training. To focus on improving discrimination between any two given acoustic models, we refine the error definition in terms of Kullback-Leibler Divergence (KLD) between them. The new measure can be regarded as a modified version of Minimum Phone Error (MPE) but with a higher resolution than just a symbol mat...