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.).

Machine learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Machine Learning and Data Mining Methods in Diabetes Research.

Let K be a field, M(n,K) be the multiplicatlve semlgroup of all n• matrices over K, GL(n,K) be the group of all invertible matrices over K. If A~ M(n,K) then by LA we denote a linear envelope of A. The set A is said to be irreducible if LA=M(n,K). In ~I] the following problem was posed (~9,problem ~): does there exist a O-simple irreducible semigroup in M(n,K) which is not completely O-simple; does there exist a simple irreducible subsemigroup of GL(a,K) which is not a group? In [2S the importance of this problem for the theory of semigroup rings was noted. The purpose of the present note is to answer both questions affirmatively. To this end we construct an example of a simple semlgroup S in GL(n,K) which is not a group. Then the semlgroup S o with adjoined zero is O-simple and is not completel~ O-simple. THEOreM. Let Q be the ~ of rational numbers. is ~ SimDle ~ ~ S of GL(2,Q) ~I f~ is not a ~ . P r o o f. Let G be a subgroup generated in GL(2,Q)

A simple matrix semigroup which is not completely simple

Cayley graphs considered as language recognisers are as powerful as the more general finite state automata. This paper applies Cayley graphs to define a class of automata and describe minimal automata of this type, all their congruences and the Nerode equivalence of states.

/pdf/labelled-cayley-graphs-and-minimal-automata-4qqznh4exm.pdf

Labelled Cayley graphs and minimal automata

Radicals and bands of associative algebras

Rapid expansion in numbers of cloud service providers (CSPs) has resulted in the introduction of various brokering services and cloud marketplaces designed to help customers to assess and choose CSPs. The problem of evaluating marketplaces and also separate sub-offerings supplied by cloud service providers (CSPs) has become vitally important because cloud marketplaces can now incorporate sub-offerings from different CSPs into a combined package tailored for each customer. In this paper, we introduce a new concept which we refer to as a cloud omnibus system (COS) and which we define as a cloud marketing system evaluating whole cloud marketplaces, CSPs, brokers and separate service sub-offerings from CSPs (sometimes combined into packages tailored for customers). COS is based on trust and management of trust and is beyond the state-of-the-art in providing a formal trust measuring mechanism for customers by introducing three types of trust which we refer to as 'direct', 'relative' and 'transparent'.

Cloud omnibus system for evaluating cloud market services

This article gives a survey of combinatorial algorithms and methods for database security related to the work of Mirka Miller. The main contributions of Mirka Miller and coauthors to the security of statistical databases include the introduction of Static Audit Expert and theorems determining time complexity of its combinatorial algorithms, a polynomial time algorithm for deciding whether the maximum possible usability can be achieved in statistical database with a special class of answerable statistics, NP-completeness of similar problems concerning several other types of databases, sharp upper bounds on the number of compromise-free queries in certain categories of statistical databases, and analogous results on applications of Static Audit Expert for the prevention of relative compromise.

/pdf/combinatorial-algorithms-and-methods-for-security-of-mtmtcjxz6w.pdf

Andrei V. Kelarev

Papers

A simple matrix semigroup which is not completely simple

Labelled Cayley graphs and minimal automata

Radicals and bands of associative algebras

Cloud omnibus system for evaluating cloud market services

Combinatorial Algorithms and Methods for Security of Statistical Databases Related to the Work of Mirka Miller