A Unified Collaborative Strategy for Data Analysis and Feature Extraction – This paper presents a new framework for a unified framework for data analysis and representation of images with different types of nonlinear nonmonotonic transformations, including color, shape, and scale, using a novel and principled approach that combines nonlinear transformations with transformation-based learning techniques. The framework provides a powerful tool to improve the accuracy of a regression equation by leveraging local transformations, and has applications in image classification, texture synthesis, and object detection via a large-scale, nonlinear transformation graph.

The use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.

The Online Stochastic Discriminator Optimizer

Classification of Mammal Microbeads on Electron Microscopy Using Fuzzy Visual Coding

A Unified Collaborative Strategy for Data Analysis and Feature Extraction

Clustering-Based Feature Representations for Person Re-Identification

Improved LSTM based Speech Recognition by Adaptive Deep LearningThe use of neural networks has made the task of speech recognition more challenging due to its inability to classify both the semantic information and the utterances of the words. In this paper, we focus on two important problems from the literature: 1. Recognition of utterances in a single speech sentence; 2. Classification of the utterances in an ensemble of speech sentences. The performance of the machine learning is evaluated using a multi-task learning (MTM) and Multi-channel (MC) learning paradigm for speech recognition. The best performance was achieved using a test set consisting of four different languages, with the goal of predicting the sentence sequence with a higher accuracy than using any other language pair.