A Novel Approach to Text Classification based on Keyphrase Matching and Word Translation – In many languages the choice of a common language partner has significant impact on the quality of a text. Here we propose a language-independent method that extracts the most useful information from text. This method is based on an evolutionary algorithm to select a partner that best captures the needs of a text. The key to this approach is the combination of two key features: (1) the target language partner with the most resources is the same language partner, and (2) the candidate partner is an intelligent agent. Our method, termed as a bilingual text classifier (BLCS), extracts the most relevant information and the most useful information from the candidate partner, based on a genetic algorithm’s approach of evolutionary design. Through experiments on both simulated and real data it was shown that it is possible to significantly improve the quality of a text, in terms of both the resources and the candidate partner for each language partner.
We report on the development of the proposed multinomial family of probabilistic models, and a comparison of their properties against the existing ones. We prove that the Bayesian multinomial family of probabilistic models is not a linear combination of two functions which is the case in both the linear family of models and the linear model by a new family of parameters. More precisely, we prove that the Bayesian multinomial family of probabilistic models is, given a set of functions of the same form, not a linear combination of a function of a function from multiple functions, which is the case in both the linear family of models and the linear model by a new family of parameters.
Deep Multi-view Feature Learning for Text Recognition
Anomaly Detection with Neural Networks and A Discriminative Labeling Policy
A Novel Approach to Text Classification based on Keyphrase Matching and Word Translation
Probabilistic Models for Robust Machine LearningWe report on the development of the proposed multinomial family of probabilistic models, and a comparison of their properties against the existing ones. We prove that the Bayesian multinomial family of probabilistic models is not a linear combination of two functions which is the case in both the linear family of models and the linear model by a new family of parameters. More precisely, we prove that the Bayesian multinomial family of probabilistic models is, given a set of functions of the same form, not a linear combination of a function of a function from multiple functions, which is the case in both the linear family of models and the linear model by a new family of parameters.