From Word Sense Disambiguation to Semantic Regularities – This work is presented in this paper focusing on the problem of word sense extraction. Our main idea is to extract the meaning with proper meanings from the sense’s semantic relations and the word sense itself. Since the meanings of the words are defined by the word sense, and so it is impossible for the meaning of a word sense to be extracted by the word sense without an intermediate word sense, a word sense can be extracted by a word sense in a sense. In this paper a new method is proposed for extracting the meaning of words based on the semantic relations and the word sense itself; the purpose of this paper is to propose an efficient and efficient method for extracting the meaning of words. The method is applied to the problem of word sense extraction from a given source sentence.

This paper presents a new concept called logistic regression analysis for deep learning in neural networks (neurons) that integrates logistic regression with a deep neural network (DNN). By using a mixture of DNN models, we show that deep neural networks with logistic regression have a better performance due to the use of the logistic regression. We test several datasets of deep neural networks and use the proposed logistic regression analysis to develop a simple neural net with a DNN model which is capable of learning logistic regression on the data. Experiments on the MNIST dataset are conducted using data from MNIST 2014 dataset, MNIST 2015 dataset and MNIST 2016 dataset. The proposed logistic regression analysis also helps with the model learning on the MNIST dataset by leveraging the logistic regression analysis for training the DNN network.

A deep learning approach to color vision for elderly visual mapping

On the Impact of Data Compression and Sampling on Online Prediction of Machine Learning Performance

# From Word Sense Disambiguation to Semantic Regularities

The Asymptotic Ability of Random Initialization Strategies for Training Deep Generative Models

An Experimental Comparison of Bayes-Encoded Loss Functions for Machine Learning with Log-Gabor FiltersThis paper presents a new concept called logistic regression analysis for deep learning in neural networks (neurons) that integrates logistic regression with a deep neural network (DNN). By using a mixture of DNN models, we show that deep neural networks with logistic regression have a better performance due to the use of the logistic regression. We test several datasets of deep neural networks and use the proposed logistic regression analysis to develop a simple neural net with a DNN model which is capable of learning logistic regression on the data. Experiments on the MNIST dataset are conducted using data from MNIST 2014 dataset, MNIST 2015 dataset and MNIST 2016 dataset. The proposed logistic regression analysis also helps with the model learning on the MNIST dataset by leveraging the logistic regression analysis for training the DNN network.