Explanation-based analysis of taxonomic information in taxonomical text – In this paper, we present an end-to-end algorithm to generate taxonomic descriptions from a corpus. We have two main objectives: (i) to extract the taxonomic units of the information in the query texts and (ii) to generate taxonomical descriptions of the information in taxonomic text that is not available in the data repositories. On the basis of our main goal, we have collected a corpus of query text from three websites: Wikipedia, Wikipedia.com, and Wikidata. The queries contain a large number of information contained in the Wikipedia.com and Wikidata database. The query text comprises a number of different categories, which are then automatically extracted by the algorithm. Using each of them, we have generated more taxonomic descriptions of English taxonomy. This yields an estimate of the taxonomic units of the information in the corpus.

The problem of estimating a given prediction is a nonlinear and non-parametric phenomenon of high nonlinearity. The classical and recent algorithms are unable to estimate prediction probability as well as prediction probability for the data, and consequently, these algorithms are largely limited to estimating a low-parameter probability distribution. In this paper, we focus on the estimation of the probability of prediction for certain conditions under a particular scenario with high nonlinearity. We propose a principled algorithm which learns Bayes Bayesian Optimality (BBA) using a priori knowledge of the probability of prediction, and we compare the algorithm to Bayesian optimization. Compared to the state of the art, our algorithm outperforms the more traditional optimization method, while outperforming the previous state of the art algorithms in terms of accuracy and time.

Matching with Linguistic Information: The Evolutionary Graphs

Generalized Bayes method for modeling phenomena in qualitative research

Explanation-based analysis of taxonomic information in taxonomical text

Sparse Bimodal Neural Networks (SimBLMN) are Predictive of Chemotypes via Subsequent Occurrence Density Estimation

A Fuzzy Rule Based Model for Predicting Performance of Probabilistic Forecasting AgentsThe problem of estimating a given prediction is a nonlinear and non-parametric phenomenon of high nonlinearity. The classical and recent algorithms are unable to estimate prediction probability as well as prediction probability for the data, and consequently, these algorithms are largely limited to estimating a low-parameter probability distribution. In this paper, we focus on the estimation of the probability of prediction for certain conditions under a particular scenario with high nonlinearity. We propose a principled algorithm which learns Bayes Bayesian Optimality (BBA) using a priori knowledge of the probability of prediction, and we compare the algorithm to Bayesian optimization. Compared to the state of the art, our algorithm outperforms the more traditional optimization method, while outperforming the previous state of the art algorithms in terms of accuracy and time.