Falsified Belief-In-A-Set and Other True Beliefs Revisited – We study the topic of belief in a set of hypotheses, and provide a general framework for learning such a set. We show that given a set of hypotheses, it is possible to identify hypotheses that are associated with a certain set of variables. This framework, called belief-in-a-set, has applications in learning and reasoning, where we demonstrate how to learn probability distributions from a set of hypotheses to predict the posterior distribution of a probability distribution.

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.

Constrained Two-Stage Multiple Kernel Learning for Graph Signals

# Falsified Belief-In-A-Set and Other True Beliefs Revisited

A Survey on Modeling Problems for Machine Learning

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