Decision Boundaries and Classification Performance Of SVM And KNN Classifiers For 2-Dimensional Dataset

Support Vector Machines (SVM) and K-Nearest Neighborhood (k-NN) are two most popular classifiers in machine learning. In this paper, we intend to study the generalization performance of the two classifiers by visualizing the decision boundary of each classifier when subjected to a two-dimensional (2-D) dataset. Four different sets of database comprising of 2-D datasets namely the eigenpostures of human (EPHuman), the breast cancer (BCancer), the Swiss roll (SRoll) and Twinpeaks (Tpeaks) were used in this study. Results obtained confirmed SVM classifier superb generalization performance since it contributed the lower classification error rate when compared to the k-NN classifier during the training for binary classification of all 2-D datasets. This is evident and can be clearly visualized through the plots depicting the decision boundaries of the binary classification task

Parental preferences and inequality within the family: evidence from Mexican siblings

This paper develops and tests a simple model of parental allocations which stresses the efficiency-equity tradeoff they face when investing in children of varying ability. The empirical component of the paper focuses on inequalities in schooling and child labour. Using sibling- based fixed effect models, I show that large differences in IQ test scores between siblings do not translate into large differences in schooling. The evidence also suggests that richer households compensate more than poorer ones. On child labour participation, the data show that there is a great deal of variation amongst brothers along this margin. If parents take compensating action they may be able to attenuate any harmful e¤ects arising from these differences. I show that in fact there are substantial adverse effects: participation in paid employment during elementary school leads to rise of 8 percentage points in the probability of being affected by one of seven acute morbidity conditions. These results suggest that even if parents act to compensate along some dimensions, in the end they may be forced to pick and choose amongst their children for other allocations, leading to large inequalities

CLASSIFICATION OF FEATURE SELECTION BASED ON ARTIFICIAL NEURAL NETWORK

Pattern recognition (PR) is the central in a variety of engineering applications. For this reason, it is indeed vital to develop efficient pattern recognition systems that facilitate decision making automatically and reliably. In this study, the implementation of PR system based on computational intelligence approach namely artificial neural network (ANN) is performed subsequent to selection of the best feature vectors. A framework to determine the best eigenvectors which we named as ‘eigenpostures’ of four main human postures specifically, standing, squatting/sitting, bending and lying based on the rules of thumb of Principal Component Analysis (PCA) has been developed. Accordingly, all three rules of PCA namely the KG-rule, Cumulative Variance and the Scree test suggest retaining only 35 main principal component or ‘eigenpostures’. Next, these ‘eigenpostures’ are statistically analyzed via Analysis of Variance (ANOVA) prior to classification. Thus, the most relevant component of the selected eigenpostures can be determined. Both categories of ‘eigenpostures’ prior to ANOVA as well as after ANOVA served as inputs to the ANN classifier to verify the effectiveness of feature selection based on statistical analysis. Results attained confirmed that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of four types of human postures

The Olfactory Nervous System Of Terrestrial And Aquatic Vertebrates

Animals in their natural milieu are surrounded by odors. These odors are rich source of information, and are perceived by sophisticated olfactory systems, that have evolved over time. The sense of smell helps species to localize prey, evade predators, explore food and recognize viable mates. In humans, memoirs, thoughts, emotions, and associations are more readily reached through the sense of smell than through any other channel. This suggests that olfactory processing is imperative and may differ fundamentally from processing in other sensory modalities. The molecular age in olfaction initiated in 1991 with the significant discovery of a large, multigene family of olfactory receptors in rat by Linda Buck and Richard Axel (Buck and Axel, 1991). The first cloned olfactory receptors consisted of a diverse repertoire of G-protein coupled receptors (GPCRs) with seven-trans membrane topology, and they were sparsely expressed in the olfactory epithelium. This Nobel Prize worthy pioneering discovery, together with availability of modern techniques and numerous completely sequenced genomes opened the way to characterize the gene families of olfactory receptors through exhaustive computational data mining in different species genome as well as by in vitro biology. In this review, I will explain about the two main model organism of olfactory perceptions, zebrafish and mouse