Recall that in the active learning setting, learner is provided with unlabeled samples, and can query teacher for the label. The goal is to learn a concept close enough to the target concept while minimizing the number of labels queried. Ideally the number of labels needed is much smaller than Ω(1/ ), which is the number of labeled examples in the passive learning setting. To characterize the s...

BACKGROUND To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural cor...

In this paper, we consider a map labeling problem to maximize the number of independent labels in the plane. We first investigate the point labeling model that each label can be placed on a given set of anchors on a horizontal line. It is known that most of the map labeling decision models on a single line (horizontal or slope line) can be easily solved. However, the label number maximization m...

INTRODUCTION Off-label drug prescribing is very common in Psychiatry. US-Food and Drug Administration has defined off-label drug as "use of drugs for the indication, dosage form, regimen, patient or other use constraint not mentioned in the approved labeling." OBJECTIVE The objective was to evaluate off-label drug use in patients attending Outpatient Department of Psychiatry. MATERIALS AND ...

A {em Roman dominating function} on a graph $G$ is a function$f:V(G)rightarrow {0,1,2}$ satisfying the condition that everyvertex $u$ for which $f(u) = 0$ is adjacent to at least one vertex$v$ for which $f(v) =2$. {color{blue}A {em restrained Roman dominating}function} $f$ is a {color{blue} Roman dominating function if the vertices with label 0 inducea subgraph with no isolated vertex.} The wei...

In many real-world domains like text categorization, supervised learning requires a large number of training examples. In our research, we are using active learning with committees methods to reduce the number of training examples required for learning. Disagreement among the committee members on the predicted label for the input part of each example is used to determine the need for knowing th...

Semi-supervised multi-label classification has been applied to many real-world applications such as image classification, document classification and so on. In semi-supervised learning, unlabeled samples are added to the training set for enhancing the classification performance, however, noises are introduced simultaneously. In order to reduce this negative effect, the nearest neighbor data edi...

We consider the problem of learning a finite automaton M of n states with input alphabet X and output alphabet Y when a teacher has helpfully or randomly labeled the states of M using labels from a set L. The learner has access to label queries; a label query with input string w returns both the output and the label of the state reached by w. Because different automata may have the same output ...