Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data set.s. VVe use mixture models for the density estimates and make two distinct appeals to the ExpectationMaximization (EM) principle (Dempster et al., 1977) in deriving a learning ...

1 Geographic information systems (GIS) are widely used for mapping wildlife movement 2 patterns, and observed wildlife locations are surrogates for inferring on wildlife 3 movement and habitat selection. We present a new approach to mapping areas where 4 wildlife exhibit sustained use, which we term slow movement areas (SMAs). Nested 5 within the habitat selection concepts of home range and cor...

We address the unsupervised domain adaptation problem for visual recognition when an auxiliary data view is available during training. This is important because it allows improving the training of visual classifiers on a new target visual domain when paired additional source data is cheaply available. This is the case when we learn from a source of RGB plus depth data, for then test on a new RG...

This paper presents a model of collaborative discourse which integrates problem-solving, discourse, dialogue initiative and connict resolution. Experimental evidence obtained from an implementation of this theory demonstrates the eeectiveness of dialogue initiative and connict resolution within the model. 1 Motivation Two recently implemented voice dialogue systems utilize the principle that di...

In randomized trials, it is typical that a number of outcome variables are collected at each follow-up time. Sometimes, a composite outcome may be of scientific interest. A composite outcome is composed as a function of several patient-specific outcomes and could measure, for example, improvement or deterioration in the condition of a patient. Multiple imputation is one method for handling pati...

Given the rapid growth of data it is important to extract mine and discover useful information from databases and data ware houses The process of data cleansing is crucial because of the garbage in garbage out principle Dirty data les are prevalent because of incorrect or missing data values inconsistent value naming conventions and incomplete information Hence we may have multiple records refe...

Seismic tomography used in the laboratory, as well as in the field, is strongly affected by limited and nonuniform ray coverage. A two-stage autoregressive extrapolation technique is proposed that can be used to extend the observed data and provide better tomographic images. The algorithm is based on the principle that the extrapolated data add minimal information to the existing data. The firs...

Several different uncertain inference systems (UISs) have been developed for representing uncertainty in rule-based expert systems. Some of these, such as Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as approximations to probability, and others, such as Fuzzy Set Theory and DempsterShafer Belief Functions were not. How different are these UISs in practice, and does i...

In this work we analyze data for 314 participants of a methadone study over 180 days. Dosages in mg were converted for better interpretability to seven categories in which six categories have an ordinal scale for representing dosages and one category for missing dosages. We develop a dissimilarity measure and cluster the time series using “partitioning around medoids” (PAM). The dissimilarity m...

The paper presents an efficient solution to decision problems where direct partial information on the distribution of the states of nature is available, either by observations of previous repetitions of the decision problem or by direct expert judgements. To process this information we use a recent generalization of Walley’s imprecise Dirichlet model, allowing us also to handle incomplete obser...