The term \bias" is widely used|and with diierent meanings|in the elds of machine learning and statistics. This paper clariies the uses of this term and shows how to measure and visualize the statistical bias and variance of learning algorithms. Statistical bias and variance can be applied to diagnose problems with machine learning bias, and the paper shows four examples of this. Finally, the pa...

A novel hybrid design based electronic voting system is proposed, implemented and analyzed. The proposed system uses two voter verification techniques to give better results in comparison to single identification based systems. Finger print and facial recognition based methods are used for voter identification. Cross verification of a voter during an election process provides better accuracy th...

In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Each neighbor of a sample to be classified is considered as an item of evidence that supports certain hypotheses regarding the class membership of that pattern. The degree of support is defined as a function of th...

In this paper, we propose a high-accuracy, highspeed method for recognizing pharmaceutical blister packs. In the proposed method, we first rank reference images of blister packs stored in a database for a given query image by using a simple voting process based on a nearest-neighbor search for local features. Next, we evaluate the results in ranking order by similarity of shape and color with r...

In this paper, we describe a new brute force algorithm for building the k-Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best algorithm. There are two main parts to th...

We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the costsensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic oneversus-one decomposition. While such a näıve reduction results in exponentiallymany classi...

Several effective methods for improving the performance of a single learning algorithm have been developed recently. The general approach is to create a set of learned models by repeatedly applying the algorithm to different versions of the training data, and then combine the learned models' predictions according to a prescribed voting scheme. Little work has been done in combining the predicti...

All the existing multi-task local learning methods are defined on homogeneous neighborhood which consists of all data points from only one task. In this paper, different from existing methods, we propose local learning methods for multitask classification and regression problems based on heterogeneous neighborhood which is defined on data points from all tasks. Specifically, we extend the knear...