With Bayesian logic networks (BLNs), we present a practical representation formalism for statistical relational knowledge. Based on the concept of mixed networks with probabilistic and deterministic constraints, BLNs combine the probabilistic semantics of (relational) Bayesian networks with constraints in first-order logic. In practical applications, efficient inference in statistical relationa...

Practical problems often combine real-world hard constraints with soft constraints involving preferences, uncertainties or flexible requirements. A probability distribution over the models that meet the hard constraints is an answer to such problems that is in the spirit of incorporating soft constraints. We propose a method using SAT-based reasoning, probabilistic reasoning and linear programm...

Complex networks is a recent area of research motivated by the empirical study of realworld networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose of this work is to develop optimization-base...

In this paper, a new support vector machine classifier with probabilistic constrains is proposed which presence probability of samples in each class is determined based on a distribution function. Noise is caused incorrect calculation of support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density funct...

In the model of choice, studied in this paper, the decision maker chooses the actions non-probabilistically in each period (Sarin and Vahid, 1999; Sarin, 2000). The action is chosen if it yields the biggest payoff according to the decision maker’s subjective assessment. Decision maker knows nothing about the process that generates the payoffs. If the decision maker remembers only recent payoffs...

The Probabilistic Data Association (PDA) problem involves identifying correspondences between items over data sequences on the basis of similarity functions. PDA has long been a topic of interest in many application areas such as tracking and surveillance; however, despite being a common and important data-analysis problem, it has largely been ignored by the database community. Our work rectifi...

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn th...

In many online applications of machine learning, the computational resources available will vary from time-to-time. Surprisingly, existing techniques are designed to accommodate the minimum expected resources, and fail to utilize further resources when they are available. This paper presents an analysis of the relevant categories of computational resource involved, and presents an algorithm tha...

Experts (human or computer) are often required to assess the probability of uncertain events. When a collection of experts independently assess events that are structurally interrelated, the resulting assessment may violate fundamental laws of probability. Such an assessment is termed incoherent. In this work we investigate how the problem of incoherence may be affected by allowing experts to s...

This paper discusses geometric programs with joint probabilistic constraints. When the stochastic parameters are normally distributed and independent of each other, we approximate the problem by using piecewise polynomial functions with non-negative coefficients, and transform the approximation problem into a convex geometric program. We prove that this approximation method provides a lower bou...