We present a graph-based semi-supervised approach for learning user-preferred travel schedules. Assuming a setting in which a user provides a small number of labeled travel schedules, we classify schedules into desirable and non-desirable. This task is non-trivial since only a small number of labeled points is available. It is further complicated by the fact that each schedule is comprised of m...

Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficult, and therefore approximate inference methods based on a linear programming ...

We present a graph-based semi-supervised approach for learning user-preferred travel schedules. Assuming a setting in which a user provides a small number of labeled travel schedules, we classify schedules into desirable and non-desirable. This task is non-trivial since only a small number of labeled points is available. It is further complicated by the fact that each schedule is comprised of m...

Eighteen mature, healthy horses were divided into three groups (six per group) receiving either no treatment, 15 consecutive days of phenylbutazone (PBZ), or 15 consecutive days of suxibuzone (SBZ) at recommended label doses. Horses underwent endoscopy before and after the treatment period and were assigned gastric ulcer scores. Gastric ulcer number and severity scores were similar across treat...

This paper presents a bi-directional resource-bounded label correcting algorithm for the traveling salesman problem with time windows, in which the objective is to minimize travel times. Label extensions and dominance start simultaneously in both forward and backward directions: the forward direction from the starting depot and the backward direction from the terminating depot. The resultant la...

The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for p...

A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of la...

The Energy Star program is the most prominent energy efficiency policy in the United States. Using data on residential clothes washers, we examine the price premium for the Energy Star label. A standard hedonic analysis reveals a number of confounding factors, including a large price premium for front loading washers. A regression discontinuity design reveals no significant price premium. Addit...

In this paper, we propose a semi-supervised cluster-and-label algorithm for utterance classification. The approach assumes that the underlying class distribution is roughly captured through– fully unsupervised–clustering. Then, a minimum number of labeled examples is used to automatically label the extracted clusters so that the initial label set is ”augmented” to the whole clustered data. The ...

Wild-type Aspergillus parasiticus produces, in addition to the colorless aflatoxins, a number of pigmented secondary metabolites. Examination of these pigments demonstrated that a major component was an anthraquinone, averufanin. Radiolabeling studies with [14C]averufanin showed that 23% of the label was incorporated into aflatoxin B1 by the wild type and that 31% of the label was incorporated ...