Word knowledge is often partial, rather than all-or-none. In this paper, we describe a method for estimating partial word knowledge on a trial-by-trial basis. Users generate a free-form synonym for a newly learned word. We then apply a probabilistic regression model that combines features based on Latent Semantic Analysis (LSA) with features derived from a large-scale, multi-relation word graph...

Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them...

We consider different types of loss functions for discrete ordinal regression, i.e. fitting labels that may take one of several discrete, but ordered, values. These types of labels arise when preferences are specified by selecting, for each item, one of several rating “levels”, e.g. one through five stars. We present two general threshold-based constructions which can be used to generalize loss...

the aim of this study was to investigate the potential association between growth hormone gh/alui and growth hormone receptor ghr/alui polymorphisms with milk yield and reproductive performances in holstein dairy cows in iran. blood samples of 150 holstein cows were collected and their genomic dna was extracted using gene-fanavaran dna extracting kit. fragments of the 428 bp of exon 5 growth ho...

Response variables in biological phenomena vary between three types: numerical response variables, ordinal categorical and nominal variables. In statistical studies, handling varies accordance with the perspective of approach to variable. Ordinal can be adopted as which neglect property categories. also treated. an variable (discrete variable), case ranking information utilized establishing pre...

Problems encountered in multiple regression due to multicolinearity or missing data can be overcome by using PLS regression. Several versions of the PLS regression algorithm exist. In this paper, we present a new version of this algorithm which can be extended to generalized linear regression models such as ordinal or multinomial logistic regression, generalized linear models, and Cox regressio...

Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...

This paper contributes to the development of the capabilities approach by showing that capabilities can, counter prevailing wisdom, be measured. By operationalising the capabilities element of Sen’s non-welfarist theory, the paper develops the data required by this novel approach to welfare economics and by exploring relations to life-satisfaction (happiness) it also examines new economic co-va...

Understanding environmental as well as anthropogenic factors that influence large herbivore ecological patterns and processes should underpin their conservation and management. We assessed the influence of intrinsic, extrinsic environmental and extrinsic anthropogenic factors on movement behaviour of eight African large herbivore species. A cumulative odds ordinal logistic regression was used t...

We examined the visual perception of joint actions, in which two individuals coordinate their body movements in space and time to achieve a joint goal. Animations of interacting action pairs (partners in human interactions) and non-interacting action pairs (individual actors sampled from different interaction sequences) were shown in the experiment. Participants were asked to rate how likely th...