Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c

Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

Background: Epilepsy is a common neurological disorder of incidence rate 1-2%. Genetic, congenital, developmental, tumors, head trauma and central nervous system infections maybe the cause of epilepsy. This study aimed to determine the prevalence of stomatitis, xerostomia and taste disorder among patients taking carbamazepine or sodium valproate and to make salivary analysis for IgA, cystatin c and salivary flow rate. Material and method:This study performed in al- Yarmuk teaching hospital in Baghdad, Samples consist of (70) epileptics half of them treated with carbamazepine and other half treated with sodium valproate, and (18) healthy control group of both genders and with different ages to detect the prevalence of oral manifestations, s

Background: Spontaneous abortion means that a pregnancy is lost prior to viability, the loss of a fetus weighing less than 500 g, and the loss of an embryo or fetus at 20 weeks gestation or less (WHO, 2001). Glenville, (2001) has reported that suffering a miscarriage is one of the most devastating things that can happen to a woman, and to her husband. Many women conceive easily and are not emotionally or physically prepared for the shock of losing a baby. Objective: To know effects of spontaneous abortion upon physical status and spiritual beliefs , also find out the association between physical status and spiritual beliefs with study variable (demographic & reproductive). Methodology: A descriptive Analytical study was conducted on Non