Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Associative Behaviours and Political Participation of EU migrants

Civic culture is structured on a network of interpersonal associations with different degrees of formalization. According to theories on civic and political action, certain agents, such as associations, play a key role in setting targets, socializing or coordinating sociopolitical actions, among other functions. Associations strengthen the political and civic system of societies. Likewise, they are a vehicle for individuals’ integration, which is particularly important in the case of immigrants. For these, associations are both a vehicle for integration and an instrument for political participation. This article explores the use and purpose of associations according to immigrants from Romania, Poland, the United Kingdom and Germany living in Spain.

Hierarchical associative memories for parallel computation /

"This report reproduces a thesis of the same title submitted to the Department of Electrical Engineering, Massachusetts Institute of Technology, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, May 1970."--p. 2

Animal intelligence; an experimetal study of the associative processes in animals.

Continued by the author's The mental life of the monkeys.

Experimental manipulation of prior experience: Effects on item and associative recognition

Frequency of exposure to very low- and high-frequency words was manipulated in a three-phase (familiarisation, study, and test) design. During familiarisation, words were presented with their definition (once, four times, or not presented). One week (Experiment 1) or one day (Experiment 2) later, participants studied a list of homogeneous pairs (i.e., pair members were matched on background and familiarisation frequency). Item and associative recognition of high- and very low-frequency words presented in intact, rearranged, old-new, or new-new pairs were tested in Experiment 1. Associative recognition of very low-frequency words was tested in Experiment 2. Results showed that prior familiaris ation improved associative recognition of very low-frequency pairs, but had no effect on high-frequency pairs. The role of meaning in the formation of item-to-item and item-to-context associations and the implications for current models of memory are discussed.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

Alcohol-related associative strength and drinking behaviours: concurrent and prospective relationships

The first part of this research assessed the longitudinal relationships between alcohol- related associative strength and alcohol use measured at two time- points, 6 months apart. Cross-lagged results support the utility of alcohol- related associative strength to predict drinking behaviours prospectively and vice versa. These results remained after competing explanations of previous use, autocorrelations between memory measures, sensation seeking and background variables of age and gender were accounted for. Findings offer further evidence for an implicit cognitions approach to drinking processes. In the second part of our study, cross-sectional analysis investigated potential mediating mechanisms in the relation of associative strength to quantity and frequency dimensions of drinking. Mediational models provide preliminary evidence that implicit memory processes may have differential effects on quantity and frequency dimensions of drinking behaviours. The results point to the possibility that increasing awareness of implicit alcohol-related associations may have utility in interventions for young adults.

Adolescent tobacco-related associative memory: a cross-sectional and contextual analysis

Tobacco use is prevalent in adolescents, and understanding factors that contribute to its uptake and early development remains a critical public health priority. Implicit drug-related memory associations (DMAs) are predictive of drug use in older samples, but such models have little application to adolescent tobacco use. Moreover, extant research on memory associations yields little information on contextual factors that may be instrumental in the development of DMAs. The present study examined (a) the degree to which tobacco-related memory associations (TMAs) were associated with concurrent tobacco use and (b) the extent to which TMAs mediated the association of peer and self-use. A sample of 210 Australian high school students was recruited. Participants completed TMA tasks and behavioral checklists designed to obscure the tobacco-related focus of the study. Results showed that TMAs were associated with peer use, and TMAs predicted self-use. We found no evidence that TMAs mediated the association of peer and self-use. Future research might examine the emotive valence of implicit nodes and drinking behavior. The results have implications for testing the efficacy of consciousness-raising interventions for adolescents at risk of tobacco experimentation or regular use.

On the annealed VC entropy for margin classifiers: A statistical mechanics study

Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.

Advances in large margin classifiers

We apply methods of Statistical Mechanics to study the generalization performance of Support vector Machines in large data spaces.

Associative knowledge controls deployment of visual selective attention

According to some models of visual selective attention, objects in a scene activate corresponding neural representations, which compete for perceptual awareness and motor behavior. During a visual search for a target object, top-down control exerted by working memory representations of the target's defining properties resolves competition in favor of the target. These models, however, ignore the existence of associative links among object representations. Here we show that such associations can strongly influence deployment of attention in humans. In the context of visual search, objects associated with the target were both recalled more often and recognized more accurately than unrelated distractors. Notably, both target and associated objects competitively weakened recognition of unrelated distractors and slowed responses to a luminance probe. Moreover, in a speeded search protocol, associated objects rendered search both slower and less accurate. Finally, the first saccades after onset of the stimulus array were more often directed toward associated than control items.

The n-tuple network: coping with sub-optimality

The subject of this thesis is the n-tuple net.work (RAMnet). The major advantage of RAMnets is their speed and the simplicity with which they can be implemented in parallel hardware. On the other hand, this method is not a universal approximator and the training procedure does not involve the minimisation of a cost function. Hence RAMnets are potentially sub-optimal. It is important to understand the source of this sub-optimality and to develop the analytical tools that allow us to quantify the generalisation cost of using this model for any given data. We view RAMnets as classifiers and function approximators and try to determine how critical their lack of' universality and optimality is. In order to understand better the inherent. restrictions of the model, we review RAMnets showing their relationship to a number of well established general models such as: Associative Memories, Kamerva's Sparse Distributed Memory, Radial Basis Functions, General Regression Networks and Bayesian Classifiers. We then benchmark binary RAMnet. model against 23 other algorithms using real-world data from the StatLog Project. This large scale experimental study indicates that RAMnets are often capable of delivering results which are competitive with those obtained by more sophisticated, computationally expensive rnodels. The Frequency Weighted version is also benchmarked and shown to perform worse than the binary RAMnet for large values of the tuple size n. We demonstrate that the main issues in the Frequency Weighted RAMnets is adequate probability estimation and propose Good-Turing estimates in place of the more commonly used :Maximum Likelihood estimates. Having established the viability of the method numerically, we focus on providillg an analytical framework that allows us to quantify the generalisation cost of RAMnets for a given datasetL. For the classification network we provide a semi-quantitative argument which is based on the notion of Tuple distance. It gives a good indication of whether the network will fail for the given data. A rigorous Bayesian framework with Gaussian process prior assumptions is given for the regression n-tuple net. We show how to calculate the generalisation cost of this net and verify the results numerically for one dimensional noisy interpolation problems. We conclude that the n-tuple method of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the analytical tools we provide.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.