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

This chapter presents the application of Computational Intelligence (CI) paradigms for supporting decision making processes. First, the three main CI techniques, i.e., evolutionary computing, fuzzy computing, and neural computing, are introduced. Then, a review of recent applications of CI-based systems for decision making in various domains is presented. The contribution of each chapter included in this book is also described. A summary of concluding remarks is presented at the end of the chapter.

Advances in Decision Making

Safety and Airworthiness of airborne platforms rest heavily on the maintainability and reliability to maximise the availability and reduce logistics down time. Most of the test and maintenance data currently produced is either paper-based or discarded and generally fails to provide preventive analysis. Improvements could be made by creating an expert system using intelligent agents. Data Mining techniques and intelligent agents could be employed to create an expert system within the Integrated Logistics Support (ILS), thereby creating a feedback mechanism. This concept would develop into an Intelligent Decision Support System (IDSS) that extrapolates forecasts and warnings. This paper presents the design and development of that agent-based Intelligent Decision Support System (IDSS).

Intelligent decision support feedback using Multi-Agent System in a defence maintenance environment

Agent coordination and cooperation co-exist in a multi-agent system (MAS), and are cognitively linked. This link is further emphasized by incorporating their atomic composition with their ability to perceive and gather information from the environment around them. To be able to possess enhanced situational awareness in an uncertain environment means improved tactical responses by combat crew. From each of their definitions and current implementations, we show how the relationship between the Belief-Desire-Intention, or BDI framework and the Observe-Orient-Decide-Act, or OODA loop can enable the use of coordinative cooperation within the agent coordination and cooperation cognitive model, or AC3M. More importantly, we show the relationship between coordination, cooperation, BDI and OODA and how this relationship can improve cognitive modeling in uncertain environments. This paper also discusses the current developments of the model and how the BDI and OODA frameworks can affect coordination and cooperation within a MAS. We recommend how these concepts can be designed and implemented for uncertain environments.

Aligning Cognitive Models Using AC3M

Scientific collaboration can be quantitatively studied by analyzing the structure and evolution of co-authorship networks. In this chapter we present a comprehensive overview of research studies focused on empirical analysis of co-authorship networks. Typical structural and evolutionary characteristics of co-authorship networks are identified by an aggregate analysis of examined studies.

https://link.springer.com/content/pdf/10.1007/978-3-319-91196-0_7

Analysis of Co-authorship Networks

We propose a strange-attractor model of tumor growth and metastasis. It is a 4-dimensional spatio-temporal cancer model with strong nonlinear couplings. Even the same type of tumor is different in every patient both in size and appearance, as well as in temporal behavior. This is clearly a characteristic of dynamical systems sensitive to initial conditions. The new chaotic model of tumor growth and decay is biologically motivated. It has been developed as a live Mathematica demonstration, see Wolfram Demonstrator site: this http URL Key words: Reaction-diffusion tumor growth model, chaotic attractor, sensitive dependence on initial tumor characteristics

Lakhmi C. Jain

Papers

Advances in Decision Making

Intelligent decision support feedback using Multi-Agent System in a defence maintenance environment

Aligning Cognitive Models Using AC3M

Analysis of Co-authorship Networks

A Mathematical Model of Chaotic Attractor in Tumor Growth and Decay