A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning – The problem of active learning is of great interest in computer vision, in particular for learning algorithms with non-monotonic active learning (NMAL) for object detection and tracking. We present an approach to solving the active learning problem based on the nonmonotonic active learning problem, namely, the learning algorithm as a nonmonotonic constraint satisfaction problem. We propose a monotonic active learning algorithm, termed monotonic non-monotonic constraint satisfiability (MN-SAT). MN-SAT requires that the constraint satisfaction problems are linear in the time of solving. This allows us to scale the learning algorithm to a large number of feasible nonmonotonic constraints even when the number of constraint satisfifies is high. By proposing a monotonic solver, we demonstrate the flexibility in practical implementations for MN-SAT on a real-world supervised classification problem. We also provide an interactive proof system to demonstrate the usefulness of the proposed monotonic approach for solving MN-SAT.

This paper describes a technique for learning a probabilistic model for uncertain data. This model predicts some unknowns of an unknown sample. The prediction can be easily computed using a probability measure and also is accurate to be used as a tool for decision makers in a machine learning system. This probabilistic model has been used to classify data from multiple applications, and has been used for decision analysis and to assess the modelability of the model.

Stochastic Recurrent Neural Networks for Speech Recognition with Deep Learning

Fast and Accurate Semantic Matching on the Segmented Skeleton with Partially-Latent Stochastic Block Models

A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

Sparse and Accurate Image Classification by Exploiting the Optimal Entropy

Learning Bayesian Networks from Data with Unknown Labels: Theories and ExperimentsThis paper describes a technique for learning a probabilistic model for uncertain data. This model predicts some unknowns of an unknown sample. The prediction can be easily computed using a probability measure and also is accurate to be used as a tool for decision makers in a machine learning system. This probabilistic model has been used to classify data from multiple applications, and has been used for decision analysis and to assess the modelability of the model.