For this project, our goal was to implement Maximum Entropy Discrimination (MED) with a linear discrimination function for binary classification purposes. This technique was presented by Jaakola, Meila, and Jebara in “Maximum Entropy Discrimination” (1999). Their paper provides a generalized framework for discrimination that relies on the maximum entropy principle. One of the interesting aspect...

Discriminative pattern mining is one of the most important techniques in data mining. This challenging task is concerned with finding a set of patterns that occur with disproportionate frequency in data sets with various class labels. Such patterns are of great value for group difference detection and classifier construction. Research on finding interesting discriminative patterns in class-labe...

Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminative information is also of importance for semisupervised feature learning. We propose a method that utilizes both the manifold structure of data and local discriminant information. Specifically, we define a local cliqu...

This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the model, i.e. its ability to rank the relevant pictures above the non-relevant ones when given a text query. In order to minimize this loss, we...

In this paper, we present a methodology for performing statistical analysis for image-based studies of differences between populations and describe our experience applying the technique in several different population comparison experiments. Unlike traditional analysis tools, we consider all features simultaneously, thus accounting for potential correlations between the features. The result of ...

objective: functional assessment of cancer therapy - breast (fact-b) scale, is widely used to measure health-related quality of life in cancer patients. the aim of the present study was to validate the fact-b in a sample of iranian women with breast cancer.   method: the sample consisted of 300 women with breast cancer that selected through non-random convenient sampling procedure from oncology...

This paper proposes a novel approach for learning discriminative and sparse representations. It consists of utilizing two different models. A predefined number of non-linear transform models are used in the learning stage and one sparsifying transform model is used at test time. The non-linear transform models have discriminative and minimum information loss priors. A novel measure related to t...