Efficiently searching the important input variables using Bayesian discriminant

Selection of important input variables for RBF network using partial derivatives

In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases. A sequential input selection algorithm for Radial basis function (SISAL-RBF) networks is presented to analyze importances of the inputs. The ranking of inputs is based on values, which are evaluated from the partial ...

Efficiently Searching for Similar Images

Searching for web information more efficiently using presentational layout analysis

Extracting and processing information from web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the web. A common approach in the extraction process is to represent a page as a ‘bag of words’ and then to perform additional processing on such a flat representation. In this paper we propose a new, hierarchical representation th...

Bayesian Quadratic Discriminant Analysis

Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier is derived using information geometry. Using a calculus of variations approach to define a functional Bre...

Hierarchical A*: Searching Abstraction Hierarchies Efficiently

Abstraction, in search, problem solving, and planning, works by replacing one state space by another (the "abstract" space) that is easier to search. The results of the search in the abstract space are used to guide search in the original space. A natural application of this technique is to use the length of the abstract solution as a heuristic for A* in searching in the original space. However...