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

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.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

How to Do Things With Words

The paper presents an analysis of chaos, limit cycles and stability in the antigen-antibody interactive dynamics. Both co-operation and competition of antibodies are considered in the dynamics. The classical approach of Lyapunov is employed here for the stability analysis of the dynamics. Computer simulations are undertaken to support the results of the analysis. Both temporal behaviors of the antibodies and their phase portraits are given to study their chaotic, limit cyclic and stable behavior. Results of stability analysis of the dynamics are applied in garbage cleaning problem by a mobile robot.

Behavioral analysis of cooperative/competitive antibody dynamics in garbage cleaning applications

This chapter presents a brief description of chapters pertaining to advanced decisions for technical and medical systems. Recent research results in the image and videos processing, transmission, and image analysis are included in Part I, while a wide spectrum of algorithms for medical image processing are included in Part II of this book. Each chapter involves detail practical implementations and explanations.

Advanced Decisions in Technical and Medical Applications: An Introduction

Recent investigations in deep learning provide a new approach for fire and smoke detection in outdoor spaces Fire and smoke detection is an additional function in smart urban surveillance, as well as, in wildlife monitoring using stationary cameras or UAV cameras Smoke detection plays an important role in a fire alarm The algorithms based on the traditional machine learning techniques show high values of errors that cause additional economic costs Deep learning techniques solve this problem partly but raise new challenges In this chapter, we analyze deep learning models applicable for this task and present a Weaved Recurrent Single Shot Detector (WRSSD) for early smoke detection with acceptable error ratios

Deep Learning for Fire and Smoke Detection in Outdoor Spaces

In this chapter we present the fundamental mathematical and cognitive scheme in which we locate the Adaptive Agent. We introduce the context definition at different orders, the transformations from one context to another and the adaptation processes by which we can establish a symmetric relation among the rules inside the different contexts. This chapter is not completely abstract but has the aim to introduce the concept of the Adaptive Agent. When we have only one context, adaptation is impossible. The definition of every context is made by variables and rules and the rules are fixed inside the context. The adaptation principle has its source within the change of the rules. Consequently because we have only one context and every change of the context is forbidden, it is not possible to have any adaptation process.

Adaptive Agents and Their Actions

Intelligent signal processing and data mining are the key components of present advances in many disciplines including science and engineering. This chapter presents a brief outline of contributions from some of the leading researchers in the field of intelligent signal processing and data mining.

Lakhmi C. Jain

Papers

Behavioral analysis of cooperative/competitive antibody dynamics in garbage cleaning applications

Advanced Decisions in Technical and Medical Applications: An Introduction

Deep Learning for Fire and Smoke Detection in Outdoor Spaces

Adaptive Agents and Their Actions

Introduction to Intelligent Signal Processing and Data Mining