Kemal Polat

Bio: Kemal Polat is an academic researcher from Abant Izzet Baysal University. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 36, co-authored 181 publications receiving 5868 citations. Previous affiliations of Kemal Polat include Selçuk University & Universiti Malaysia Perlis.

A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine

Abstract: The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.