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

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1-3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Matrix Factorization Techniques for Recommender Systems

In general, the analysis of microarray data requires two steps: feature selection and classification. From a variety of feature selection methods and classifiers, it is difficult to find optimal ensembles composed of any feature-classifier pairs. This paper proposes a novel method based on the evolutionary algorithm (EA) to form sophisticated ensembles of features and classifiers that can be used to obtain high classification performance. In spite of the exponential number of possible ensembles of individual feature-classifier pairs, an EA can produce the best ensemble in a reasonable amount of time. The chromosome is encoded with real values to decide the weight for each feature-classifier pair in an ensemble. Experimental results with two well-known microarray datasets in terms of time and classification rate indicate that the proposed method produces ensembles that are superior to individual classifiers, as well as other ensembles optimized by random and greedy strategies.

/pdf/an-evolutionary-algorithm-approach-to-optimal-ensemble-4fw8d64m4s.pdf

An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

/pdf/a-comprehensive-overview-of-the-applications-of-artificial-58avwtauyz.pdf

A Comprehensive Overview of the Applications of Artificial Life

Genetic algorithms (GAs) are probabilistic optimization methods based on the biological principle of natural evolution. One of the important operators in GAs is the selection strategy for obtaining better solutions. Specifically, finding a balance between the selection pressure and diversity is a critical issue in designing an efficient selection strategy. To this extent, the recently proposed real world tournament selection (RWTS) method has showed good performance in various benchmark problems. In this paper, we focus on analyzing characteristics of RWTS from the viewpoint of both the selection probabilities and stochastic sampling properties in order to provide a rational explanation for why RWTS provides improved performance. Statistical experimental results show that RWTS has a higher selection pressure with a relatively small loss of diversity and higher sampling accuracy than conventional tournament selection. The performance tests in a traveling salesman problem further confirm that the comparatively higher pressure and sampling accuracy, which are inherent in RWTS, can enhance the performance in the selection strategy.

Statistical properties analysis of real world tournament selection in genetic algorithms

http://sclab.yonsei.ac.kr/publications/Papers/IJ/neuro2006-bio.pdf

Ensemble classifiers based on correlation analysis for DNA microarray classification

Traffic sign detection is a useful application for driving assistance systems, and it is necessary to accurately detect traffic signs before they can be identified. Sometimes, however, it is difficult to detect traffic sign, which may be obscured by other objects or affected by illumination or lightning reflections. Most previous work on this topic has been based on region of interest analysis using the color information of traffic signs. Although this provides a simple way to segment signs, this approach is weak when a sign is affected by illumination or its own color information is distorted. To overcome this, this paper introduces a robust traffic detection framework for cluttered scenes or complex city views that does not use color information. Moreover, the proposed method can detect traffic sign in the night. We establish an edge-adaptive Gabor function, which is derived from human visual perception. It is an enhanced version of the original Gabor filter, and filters out unnecessary information to provide robust recognition. It decomposes the directional information of objects and reflects specific shapes of traffic signs. Once the extracted feature is obtained, a support vector machine detects the traffic sign. Applying scale-space theory, it is possible to resolve the scaling problem of the objects that we want to find. Our system shows robust performance in traffic sign detection, and experiments on real-world scenes confirmed its properties.

Kyung-Joong Kim

Papers

An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis

A Comprehensive Overview of the Applications of Artificial Life

Statistical properties analysis of real world tournament selection in genetic algorithms

Ensemble classifiers based on correlation analysis for DNA microarray classification

Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection