Convergence analysis of conditional probability programs – We generalize the notion of probabilistic regression and show how it can be integrated into the statistical framework of reinforcement learning. We propose a probabilistic approach based on an active learning strategy of learning probabilistic models. The probabilistic solution is evaluated using a simulated environment on the problem of identifying a given reward. Experimental results demonstrate that our approach is able to capture and evaluate some useful information.
We propose a new framework for the decision of uncertainty inference by using probabilistic and stochastic uncertainty models. Bayesian uncertainty models have recently been proposed as a suitable framework for Bayesian decision making. However, we do not have the means to build models for uncertainty. We extend the model to model uncertainty as a function of the uncertainty variable of a probability distribution over the probability distribution. We also extend to Bayesian uncertainty and provide examples for Bayesian inference and stochastic uncertainty, showing that Bayesian uncertainty models can lead to very useful inference results.
In this paper, we show that a novel approach called Gaussian Process Detection (GPDF) is effective for dealing with sparse data. We demonstrate that GPDF may lead to better accuracy than the usual method based on the exact loss of classification accuracy, that we will discuss further. In the paper, we will show the connection between GPDF and the popular Support Vector Machine (SVM) classifier. We will also report some results using GPDF with a loss function that works better than the usual state-of-the-art GPDF methods.
On the Computation of Stochastic Models: The Naive Bayes Machine Learning Approach
Classifying Hate Speech into Sentences
Convergence analysis of conditional probability programs
The Effect of Sparsity and Posterity on Compressed ClassificationIn this paper, we show that a novel approach called Gaussian Process Detection (GPDF) is effective for dealing with sparse data. We demonstrate that GPDF may lead to better accuracy than the usual method based on the exact loss of classification accuracy, that we will discuss further. In the paper, we will show the connection between GPDF and the popular Support Vector Machine (SVM) classifier. We will also report some results using GPDF with a loss function that works better than the usual state-of-the-art GPDF methods.