A large class of commonly used probabilistic models known as log-linear models are defined up to a normalization constant.Typical learning algorithms for such models require solving a sequence of probabilistic inference queries. These inferences are typically intractable, and are a major bottleneck for learning models with large output spaces. In this paper, we provide a new approach for amorti...

the operation of a distribution system in the presence of distributed generation systems has someadvantages and challenges. optimal sizing and siting of dg systems has economic, technical, andenvironmental benefits in distribution systems. improper selection of dg systems can reduce theseadvantages or even result in deterioration in the normal operation of the distribution system. dgallocation ...

It is well known that the GaussNewton algorithm for solving nonlinear least squares problems is a special case of the scoring algorithm for maximizing log likelihoods. What has received less attention is that the computation of the current correction in the scoring algorithm in both its line search and trust region forms can be cast as a linear least squares problem. This is an important observ...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...

pearl millet has tolerance to harsh growing conditions such as drought. it is at least equivalent to maize and generally superior to sorghum in protein content and metabolizable energy levels. thus it is of importance for poultry feeding. amino acid (aa) determination is expensive and time consuming. therefore nutritionists have prompted a search for alternatives to estimate aa levels. traditio...

basically, medical diagnosis problems are the most effective component of treatment policies. recently, significant advances have been formed in medical diagnosis fields using data mining techniques. data mining or knowledge discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. in this paper, bayesian classifier is used as a non-linear dat...

Given a large collection of time-evolving activities, such as Google search queries, which consist of d keywords/activities for m locations of duration n, how can we analyze temporal patterns and relationships among all these activities and find location-specific trends? How do we go about capturing non-linear evolutions of local activities and forecasting future patterns? For example, assume t...