Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Information Society, Teaching, and Learning: Education Reforms in Brazil

This article examines the subject matter of learning within the context of information society, through an inquiry concerning both the reforms in education adopted in Brazil in the last thirty years and their results. It provides a revision on the explanations of school failure based on assumptions of learning problems due to cognitive and linguistic deficits. From the guidelines related with written school forms as well as the constant cultural oppression accomplished inside the school, the article claims the necessity of changing the psychological and pedagogic views that, under the label of democratic practices, determine school institutions and its daily life, by means of instrumental relations with knowledge that disregard the reading practices which are congenial to popular culture.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcriptional responses of host peripheral blood cells to tuberculosis infection

Host responses following exposure to Mycobacterium tuberculosis (TB) are complex and can significantly affect clinical outcome. These responses, which are largely mediated by complex immune mechanisms involving peripheral blood cells (PBCs) such as T-lymphocytes, NK cells and monocyte-derived macrophages, have not been fully characterized. We hypothesize that different clinical outcome following TB exposure will be uniquely reflected in host gene expression profiles, and expression profiling of PBCs can be used to discriminate between different TB infectious outcomes. In this study, microarray analysis was performed on PBCs from three TB groups (BCG-vaccinated, latent TB infection, and active TB infection) and a control healthy group. Supervised learning algorithms were used to identify signature genomic responses that differentiate among group samples. Gene Set Enrichment Analysis was used to determine sets of genes that were co-regulated. Multivariate permutation analysis (p < 0.01) gave 645 genes differentially expressed among the four groups, with both distinct and common patterns of gene expression observed for each group. A 127-probeset, representing 77 known genes, capable of accurately classifying samples into their respective groups was identified. In addition, 13 insulin-sensitive genes were found to be differentially regulated in all three TB infected groups, underscoring the functional association between insulin signaling pathway and TB infection. Published by Elsevier Ltd.

Allergic rhinitis in the child and associated comorbidities

Allergic rhinitis (AR) typically presents after the second year of life, but the exact prevalence in early life is unknown. AR affects 10-30% of the population, with the greatest frequency found in children and adolescents. It appears that the prevalence has increased in the pediatric population. As the childs` immune system develops between the 1st and 4th yr of life, those with an atopic predisposition begin to express allergic disease with a clear Th(2) response to allergen exposure, resulting in symptoms. In pediatric AR, two or more seasons of pollen exposure are generally needed for sensitization, so allergy testing to seasonal allergens (trees, grasses, and weeds) should be conducted after the age of 2 or 3 years. Sensitization to perennial allergens (animals, dust mites, and cockroaches) may manifest several months after exposure. Classification of AR includes measurement of frequency and duration of symptoms. Intermittent AR is defined as symptoms for < 4 days/wk or < 4 consecutive weeks. Persistent AR is defined as occurring for more than 4 days/wk and more than 4 consecutive weeks. AR is associated with impairments in quality of life, sleep disorders, emotional problems, and impairment in activities such as work and school productivity and social functioning. AR can also be graded in severity - either mild or moderate/severe. There are comorbidities associated with AR. The chronic effects of the inflammatory process affect lungs, ears, growth, and others. AR can induce medical complications, learning problems and sleep-related complaints, such as obstructive sleep apnea syndrome and chronic and acute sinusitis, acute otitis media, serous otitis media, and aggravation of adenoidal hypertrophy and asthma.

Further delineation of the 15q13 microdeletion and duplication syndromes: a clinical spectrum varying from non-pathogenic to a severe outcome

Background: Recurrent 15q13.3 microdeletions were recently identified with identical proximal (BP4) and distal (BP5) breakpoints and associated with mild to moderate mental retardation and epilepsy. Methods: To assess further the clinical implications of this novel 15q13.3 microdeletion syndrome, 18 new probands with a deletion were molecularly and clinically characterised. In addition, we evaluated the characteristics of a family with a more proximal deletion between BP3 and BP4. Finally, four patients with a duplication in the BP3-BP4-BP5 region were included in this study to ascertain the clinical significance of duplications in this region. Results: The 15q13.3 microdeletion in our series was associated with a highly variable intra-and inter-familial phenotype. At least 11 of the 18 deletions identified were inherited. Moreover, 7 of 10 siblings from four different families also had this deletion: one had a mild developmental delay, four had only learning problems during childhood, but functioned well in daily life as adults, whereas the other two had no learning problems at all. In contrast to previous findings, seizures were not a common feature in our series (only 2 of 17 living probands). Three patients with deletions had cardiac defects and deletion of the KLF13 gene, located in the critical region, may contribute to these abnormalities. The limited data from the single family with the more proximal BP3-BP4 deletion suggest this deletion may have little clinical significance. Patients with duplications of the BP3-BP4-BP5 region did not share a recognisable phenotype, but psychiatric disease was noted in 2 of 4 patients. Conclusions: Overall, our findings broaden the phenotypic spectrum associated with 15q13.3 deletions and suggest that, in some individuals, deletion of 15q13.3 is not sufficient to cause disease. The existence of microdeletion syndromes, associated with an unpredictable and variable phenotypic outcome, will pose the clinician with diagnostic difficulties and challenge the commonly used paradigm in the diagnostic setting that aberrations inherited from a phenotypically normal parent are usually without clinical consequences.

Characterizing the phenotypic manifestations of MFN2 R104W mutation in Charcot-Marie-Tooth type 2

Mutations of the mitofusin 2 (MFN2) gene have been reported to be the most common cause of the axonal form of Charcot Marie Tooth disease (CMT). The aim of this study was to describe a de novo MFN2 p.R104W mutation and characterize the associated phenotype. We screened the entire coding region of MFN2 gene and characterized its clinical phenotype, nerve conduction studies and sural nerve biopsy. Neuropsychological tests and brain MRI were also performed. A de nova mutation was found in exon 4 (c.310C > T; p.R104W). In addition to a severe and early onset axonal neuropathy, the patient presented learning problems, obesity, glucose intolerance, leukoencephalopathy, brain atrophy and evidence of myelin involvement and mitochondrial structural changes on sural nerve biopsy. These results suggest that MFN2 p.R104W mutation is as a hot-spot for MFN2 gene associated to a large and complex range of phenotypes. (C) 2011 Elsevier B.V. All rights reserved.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Clinical relationships and ethical problems in primary care, Sao Paulo, SP, Brazil

Objectives: Main Objective: to identify ethical problems in primary care according to nurses` and doctors` perceptions. Secondary Objective: to know ethical issues of patient-professional relationships in primary care. Design: Synthesis to integrate and reinterpret primary results of qualitative studies. Setting: Primary healthcare centers, Sao Paulo, SP, Brazil. Participants and/or context: Incidental sample of 34 nurses and 36 medical doctors working in primary healthcare centers selected by convenience. Methods: Individual, semi-structured interviews to identity situations considered as sources of ethical problems. The sample is socially representative of primary care health centers and professionals. Data collection assured discourse saturation. Hermeneutic-dialectical discourse analysis was used to study the results. Results: Patient-professional relationships and team work were the main sources of ethical problems. The most important problems were patient information, privacy, confidentiality, interpersonal relationship, linkage and patient autonomy. These issues reflect the recent changes in clinical relation ships and show the peculiarities of primary care with its continuous care which lasts a long time. Healthcare involves multiprofessional team work in the midst of the patient claims for autonomy. Good care of patients needs requires a relationship based on communication and cooperation, and includes feelings and values, with communication skills. Conclusions: Ethical problems in primary care are common situations. For quality and humane primary care the relationship should consist of dialogue, trust and cooperation. (C) 2009 Elsevier Espana, S.L. All rights reserved.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

Evolutionary tuning of SVM parameter values in multiclass problems

Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved.

HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.