While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture

vector machine, rough set based minority class oriented rule learning methods, cost sensitive classifier perform good on imbalanced data set. We observed that current research in imbalance data problem is moving to hybrid algorithms.

Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive

Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by uti-lizing the raw output of image search engines available

.g. the Corel world, the Caltech-101 world, the PASCAL VOC world). With the focus on beating the latest benchmark numbers on the latest dataset, have we perhaps lost sight of the original purpose? The goal of this paper is to take stock of the current state of recognition datasets. We present a comparison study

We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative

Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classi-fication algorithms require that all or a por-tion of the the entire dataset remain perma-nently in memory. This limits their suitability for mining over large databases

this area of research. In this paper, we propose a set of quality met-rics for evaluating and analyzing the vi-sion & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been us-ing more complex language and more ab-stract concepts, however

Scene categorization is a fundamental problem in com-puter vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object cate-gorization contain

-isting resources to which it is linked, and the fact that it is the largest current research dataset in our field. As an illustra-tion, we present year prediction as an example application, a task that has, until now, been difficult to study owing to the absence of a large set of suitable data. We show positive