We present a way of approximating the posterior probability of a rule-set model that is comprised of a set of class descriptions. Each class description, in turn, consists of a set of relational rules. The ability to compute this posterior and to learn many models from the same training set allows us to approximate the expectation that an example to be classi ed belongs to some class. The examp...

Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotate...

Recent growth in the use of email for communication and the corresponding growth in the volume of email received has made automatic processing of email desirable. Two learning methods, naı̈ve bayesian learning with bag-valued features and the RIPPER rule-learning algorithm have shown promise in other text categorization tasks. I present three experiments in automatic mail foldering and spam filt...

In this paper, we study the design of core-selecting payment rules for combinatorial auctions (CAs), a challenging setting where no strategyproof rules exist. We observe that in many real-world CAs, bidders are heterogeneous in size and value. Unfortunately, the rule most commonly used in practice, the Quadratic rule (Day and Cramton, 2012), significantly favors large over small bidders in term...

As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naïve Bayes classif...

In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized to...

A b s t r a c t As part of the 2006 i2b2 NLP Shared Task, we explored two methods for determining the smoking status of patients from their hospital discharge summaries when explicit smoking terms were present and when those same terms were removed. We developed a simple keyword-based classifier to determine smoking status from de-identified hospital discharge summaries. We then developed a Naï...

In this paper we present a simple to implement truly online large margin version of the Perceptron ranking (PRank) algorithm, called the OAP-BPM (Online Aggregate Prank-Bayes Point Machine) algorithm, which finds a rule that correctly ranks a given training sequence of instance and target rank pairs. PRank maintains a weight vector and a set of thresholds to define a ranking rule that maps each...

Contextual bandits, and in general informed decision making, can be studied in the general stochastic/statistical setting by means of the conditional probability paradigm where Bayes’ theorem plays a central role. However, when informed decisions have to be made considering very large contextual information or the information is contained in too many variables with large history of observations...