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 term Data Mining grew from the relentless growth of techniques used to interrogation masses of data. As a myriad of databases emanated from disparate industries, management insisted their information officers develop methodology to exploit the knowledge held in their repositories. The process of extracting this knowledge evolved as an interdisciplinary field of computer science within academia. This included study into statistics, database management and Artificial Intelligence (AI). Science and technology provide the stimulus for an extremely rapid transformation from data acquisition to enterprise knowledge management systems.

Data Mining Techniques in Clustering, Association and Classification

Cardiovascular disease is a condition that causes damage to the heart muscle, valves, rhythm, or blockage in the blood vessels. It requires early diagnosis, as it is the leading cause for the sudden death in humans. Electrocardiogram (ECG) is the most important biomedical signal used extensively by the cardiologist to diagnose cardiovascular disease. The classification of ECG signals helps physicians to make decisions in the diagnosis of cardiac diseases. There are many conventional machine learning and deep learning algorithms used in the literature for the automatic classification of ECG signals. Conventional machine learning algorithms require handcrafted features. There are many features such as morphological feature extraction, computation of RR interval, QRS peak detection, ST segment, ST distance, and amplitude computation. The classical machine learning algorithms used to classify the extracted features are shallow neural network, K nearest neighbor, support vector machines (SVM), random forest, and decision tree. Deep learning algorithms learn the features from the given training data, which outperformed the handcrafted features used in the conventional machine learning algorithms. There are state-of-the-art architectures such as convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The existing architectures rely on disease-specific approach. This chapter aims to provide a single architecture by transferable approach for cardiac disease classification using ECG. We plan to achieve this by the analysis of the different state-of-the-art deep learning architectures such as CNN, LSTM, RNN, and GRU to classify cardiac diseases using ECG signal. Arrhythmia, myocardial infarction, and atrial fibrillation are the cardiac diseases we considered for the study. We have used publically available datasets from Kaggle and PhysioNet for experimental evaluation. In the proposed methodology, the deep learning architectures such as RNN, LSTM, and GRU trained to classify the atrial fibrillation from the ECG signal were used to classify other cardiac diseases such as arrhythmia and myocardial infarction. Similarly, CNN, which is trained to classify arrhythmia and myocardial infarction, is used to classify atrial fibrillation. We kept the network parameters (also known as hyperparameters) such as learning rate, batch size, and number of epochs the same for the entire experimental analysis. We evaluated the performance of the proposed methodology using the metrics: precision, recall, and F1 score. We observed that LSTM and GRU performed well compared to the RNN and CNN. LSTM and GRU offer the maximum precision and recall score, which varies between 0.97–0.98 for all three diseases. The computational cost of GRU is less compared to the RNN. Our results show that the deep learning architectures are transferable. Unlike deep learning, which is the data-driven approach, the machine learning algorithms are not adaptable or transferable. To validate this, comparison between the different machine learning algorithms like Naive Bayes, K-nearest neighbor, SVMs with two different kernels such as linear and RBF, AdaBoost, random forest, decision tree, and logistic regression are conducted for all three diseases considered for the study.

Transferable approach for cardiac disease classification using deep learning

Integrated logistics support (ILS) systems are becoming more sophisticated as the defense operations demand increasing levels of service from contractors and maintainers. Intelligent decision support system (IDSS) are required to provide adaptive automated responses for provisioning and maintenance of an increasing number of defense platforms that are now being supported either by defense organizations or by long term maintenance contracts from within the private sector. Traditional methods of repair are being upgraded to include data mining and intelligent agents to help stimulate intelligent decision support systems in creating a more efficient and reliable maintenance environment for defense. A concept demonstration, called the automated test equipment multi-agent system (ATEMAS), is being developed under a collaborative program between Defense Science and Technology Organization (DSTO), the University of South Australia and Raytheon Australia. It is envisaged that when developed, this system will be able to provide cognitive intelligence to support better reliability predictions and thus making possible the much needed proactive obsolescence management for avionics parts

Intelligent Decision Support System in Defense Maintenance Methodologies

Normally a decision support system is build to solve problems where multi-criteria decisions are involved. The knowledge base is the vital part of the decision support system containing the information or data that is used in decision-making process. This is the field where engineers and scientists have applied several intelligent techniques and heuristics to obtain optimal decisions from imprecise information. In this paper, we present a hybrid neurogenetic learning approach for the adaptation of a Mamdani fuzzy inference system for the Tactical Air Combat Decision Support System (TACDSS). Some simulation results demonstrating the different learning techniques are also provided.

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Adaptation of a Mamdani Fuzzy Inference System Using Neuro-Genetic Approach for Tactical Air Combat Decision Support System

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this chapter, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.

Lakhmi C. Jain

Papers

Data Mining Techniques in Clustering, Association and Classification

Transferable approach for cardiac disease classification using deep learning

Intelligent Decision Support System in Defense Maintenance Methodologies

Adaptation of a Mamdani Fuzzy Inference System Using Neuro-Genetic Approach for Tactical Air Combat Decision Support System

Soft Computing Paradigms and Regression Trees in Decision Support Systems