Optimizing parameter selection in Datalog transformations – We propose a new method for minimizing the loss of the parameters, via maximizing their regret in terms of the expected regret squared. This strategy is especially well suited for situations where the loss is not sensitive to the transformation’s behavior, such as when the transformation is a morphologically rich structure, or when the transformation is an optimization problem. Specifically, this strategy makes use of the notion of the least-squares minimizer when learning the parameters from data, and uses it to guarantee the optimality of the minimizer, which is the result of a priori assumptions. We apply this strategy to transform prediction by using both the maximum and minimum-margin assumptions, and apply this strategy to Datalog predictions from the same data. Our results suggest that it is possible to obtain a more natural optimization-inducing minimizer: a minimizer which maximizes the risk of the model over the space of the minimizers. Based on this optimization-inducing minimizer, our algorithm minimizes a risk of $1-$f$.

The challenge of non-linear model selection in supervised learning is a major drawback in many machine learning applications. In this paper, we propose a novel method for non-linear model selection and design, which is based on the belief vector method. Our theoretical analysis shows that the belief vector method achieves a satisfactory performance over linear models under a constant number of parameters, and outperforms most current knowledge based algorithm on a factor of 1.5. A validation experiment using standard benchmark data shows that the belief vector method can improve the performance of several algorithms by at least 10 times.

Machine Learning for the Classification of High Dimensional Data With Partial Inference

A Survey of Recent Developments in Automatic Ontology Publishing and Persuasion Learning

Optimizing parameter selection in Datalog transformations

Learning Deep Models Using Random Low Rank Tensor Factor Analysis

A new approach to the classification of noisy time-series dataThe challenge of non-linear model selection in supervised learning is a major drawback in many machine learning applications. In this paper, we propose a novel method for non-linear model selection and design, which is based on the belief vector method. Our theoretical analysis shows that the belief vector method achieves a satisfactory performance over linear models under a constant number of parameters, and outperforms most current knowledge based algorithm on a factor of 1.5. A validation experiment using standard benchmark data shows that the belief vector method can improve the performance of several algorithms by at least 10 times.