Kernel based machine learning techniques have been widely used to tackle problems of function approximation and regression estimation. Relevance vector machine (RVM) has state of the art performance in sparse regression. As a popular and competent kernel function in machine learning, conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic...

In practice, a robust solution is more appealing than an optimal solution. The methodology adds risk analysis and bootstrapping. The case study concerns pull production-control systems.Abstract Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the 'optimum' solution to be of greater importance. Therefore this paper proposes a pract...

SELECTING SPATIAL SCALE OF AREA-LEVEL COVARIATES IN REGRESSION MODELS By Lauren P. Grant, Ph.D. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2016 Director: David C. Wheeler, Ph.D., MPH Assistant Professor, Department of Biostatistics Director: Chris Gennings, Ph.D...

Fragment-based methods are quite popular in 2D QSAR/QSPR studies. In the advanced versions of these approaches for developing highly predictive models, one have to generate a huge set of descriptors that in turn requires well-designed algorithms and high-quality parallelism. To overcome these problems we developed the software for tagged generation of fragmental descriptors. One of the most per...

in this paper, we present a multi-formalism modeling framework (abbreviated by mfmf) for modeling and simulation. the proposed framework is defined based on the concepts of meta-models and uses object-orientation to overcome the complexities and to enhance the extensibility. the framework can be used as a basis for modeling by various formalisms and to support model composition in a unified man...

AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present ...

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small numbe...

Structured prediction is the problem of learning a function that maps structured inputs to structured outputs. Prototypical examples of structured prediction include part-ofspeech tagging and semantic segmentation of images. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called HC-Search. Given a structured input, t...

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and ad...

We present a novel and flexible approach to the problem of feature selection, called grafting. Rather than considering feature selection as separate from learning, grafting treats the selection of suitable features as an integral part of learning a predictor in a regularized learning framework. To make this regularized learning process sufficiently fast for large scale problems, grafting operat...