The probabilistic graphs framework models the uncertainty inherent in real-world domains by means of probabilistic edges whose value quantifies the likelihood of the edge existence or the strength of the link it represents. The goal of this paper is to provide a learning method to compute the most likely relationship between two nodes in a framework based on probabilistic graphs. In particular, given a probabilistic graph we adopted the language-constraint reachability method to compute the probability of possible interconnections that may exists between two nodes. Each of these connections may be viewed as feature, or a factor, between the two nodes and the corresponding probability as its weight. Each observed link is considered as a positive instance for its corresponding link label. Given the training set of observed links a L2-regularized Logistic Regression has been adopted to learn a model able to predict unobserved link labels. The experiments on a real world collaborative filtering problem proved that the proposed approach achieves better results than that obtained adopting classical methods.

We consider the high-dimensional heteroscedastic regression model, where the mean and the log variance are modeled as a linear combination of input variables. Existing literature on high-dimensional linear regres- sion models has largely ignored non-constant error variances, even though they commonly occur in a variety of applications ranging from biostatis- tics to finance. In this paper we study a class of non-convex penalized pseudolikelihood estimators for both the mean and variance parameters. We show that the Heteroscedastic Iterative Penalized Pseudolikelihood Optimizer (HIPPO) achieves the oracle property, that is, we prove that the rates of convergence are the same as if the true model was known. We demonstrate numerical properties of the procedure on a simulation study and real world data.

We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. The equivalence holds when energy distances are computed with semimetrics of negative type, in which case a kernel may be defined such that the RKHS distance between distributions corresponds exactly to the energy distance. We determine the class of probability distributions for which kernels induced by semimetrics are characteristic (that is, for which embeddings of the distributions to an RKHS are injective). Finally, we investigate the performance of this family of kernels in two-sample and independence tests: we show in particular that the energy distance most commonly employed in statistics is just one member of a parametric family of kernels, and that other choices from this family can yield more powerful tests.

In this paper, we outline a system for evaluating the performance of scientific research across a number of outcome metrics (e.g. publications, sales, new hires). Our system is designed to classify research performance into a number of metrics, evaluate each metric’s performance using only data on other metrics, and to cast predictions of future performance by metric. This study shows how data mining techniques can be used to provide a predictive analytic approach to the management of resources for scientific research.

In this paper, we propose a model for semantic clustering of entities extracted from a text, and we apply it to a Proper Noun classification task.This model is based on a new method to compute the similarity between the entities.Indeed, the classical way of calculating similarity is to build a feature vector or Bag-of-Features for each entity and then use classical similarity functions like Cosine.In practice, the features are contextual, such as words around the different occurrences of each entity. Here, we propose to use an alternative representation for entities, called Bag-of-Vectors, or Bag-of-Bags-of-Features.In this new model, each entity is not defined as a unique vector but as a set of vectors, in which each vector is built based on the contextual features of one occurrence of the entity.In order to use Bag-of-Vectors for clustering, we introduce new versions of classical similarity functions such as Cosine and Scalar Products. Experimentally, we show that the Bag-of-Vectors representation always improve the clustering results compared to classical Bag-of-Features representations.

A possible approach to extend classical logics to probabilistic logics is to consider a probability distribution over the classical interpretations that satisfies some constraints and maximizes entropy. Over the past years miscellaneous languages and semantics have been considered often based on similar ideas. In this paper a hierarchy of general probabilistic semantics is developed. It incorporates some interesting specific semantics and a family of standard semantics that can be used to extend arbitrary languages with finite interpretation sets to probabilistic languages. We use the hierarchy to generalize an approach reducing the complexity of the whole entailment process and sketch the importance for further theoretical and practical applications.

Our motivation believe our results will be of special interest to researchers for this work comes from the fact that with the large from the field of medical image analysis, as these images amounts of data that the SDO mission started transmitting, seem to be the closest to our dataset [21. We also identify hand labeling (commonly used by solar physicist in the last some interesting combinations of dimensionality reduction decades) of these images is simply impossible. There have methods and classifiers that behave differently across the been several successful CBIR systems for medical images presented datasets. Our research problem in Solar physics is [2] as well as in other domains [3]; none of them, however, of great practical relevance for Earth's climate since solar have dealt with the volume of data that the SDO mission flares endanger the lives of passengers on commercial airline generates. This NASA mission, only with its Atmospheric routes going over the poles, interrupt radio communications Imaging Assembly (AIA), generates eight 4096 pixels x in bands the military uses, can (and have) knocked 4096 pixels images every 10 seconds. This leads to a data down power grids. The systematic feature recognition and transmission rate of approximately 700 gigabytes per day the study of the metadata, is a key component of the ultimate only from the AIA component (the entire mission is expected prediction of solar activity (space weather).

The Support Vector Machine has gained significant popularity over recent years as a kernel-based supervised learning technique. However, choosing the appropriate kernel function and its associated parameters is not a trivial task. The kernel is often chosen from several widely-used and general-purpose functions, and the parameters are then empirically tuned for the best results on a specific data set. This paper explores the use of Particle Swarm Optimization and Genetic Programming as evolutionary approaches to evolve effective kernel functions for a given dataset. Rather than using expert knowledge, we evolve kernel functions without human-guided knowledge or intuition. Our results show consistently better SVM performance with evolved kernels over a variety of traditional kernels on several datasets.

Decision making has proposed multiple methods to help the decision maker in his analysis, by suggesting ways of formalization of the preferences as well as the assessment of the uncertainties. Although these techniques are established and proven to be mathematically sound, experience has shown that in certain situations we tend to avoid the formal approach by acting intuitively. Especially, when the decision involves a large number of attributes and outcomes, and where we need to use pragmatic and heuristic simplifications such as considering only the most important attributes and omitting the others. In this paper, we provide a model for decision making in situations subject to a large predictive uncertainty with a small learning sample. The high predictive uncertainty is concretized by a countably infinite number of prospects, making the preferences assessment more difficult. Our main result is an extension of the Maximum Entropy utility (MEU) principle into an asymptotic maximum entropy utility principle for preferences elicitation. This will allow us to overcome the limits of the existing MEU method to the extend that we focus on utility assessment when the set of the available discrete prospects is countably infinite. Furthermore, our proposed model can be used to analyze situations of high-cognitive load as well as to understand how humans handle these problems under Ceteris Paribus assumption.

Many studies have been conducted on how to detect emotion classes or magnitudes from multimedia information such as text, audio, and images. However, the methods that can use predicted emotion classes and magnitudes to render emotion expressions in Embodied Conversational Agents (ECA) are still unclear. This paper proposes a computer graphics methodology that uses predicted non-linear regression values to render facial expressions using mesh morphing techniques. Results of the rendering technique are presented and discussed.