Zhang Y. (ed.) Application of Machine Learning

InTech, 2010, -288 p.In recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user’s reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques such as rule induction, neural networks, genetic learning, case-based reasoning, and analytic learning have been widely applied to real-world problems. Machine Learning employs learning methods which explore relationships in sample data to learn and infer solutions. Learning from data is a hard problem. It is the process of constructing a model from data. In the problem of pattern analysis, learning methods are used to find patterns in data. In the classification, one seeks to predict the value of a special feature in the data as a function of the remaining ones. A good model is one that can effectively be used to gain insights and make predictions within a given domain.
General speaking, the machine learning techniques that we adopt should have certain properties for it to be efficient, for example, computational efficiency, robustness and statistical stability. Computational efficiency restricts the class of algorithms to those which can scale with the size of the input. As the size of the input increases, the computational resources required by the algorithm and the time it takes to provide an output should scale in polynomial proportion. In most cases, the data that is presented to the learning algorithm may contain noise. So the pattern may not be exact, but statistical. A robust algorithm is able to tolerate some level of noise and not affect its output too much. Statistical stability is a quality of algorithms that capture true relations of the source and not just some peculiarities of the training data. Statistically stable algorithms will correctly find patterns in unseen data from the same source, and we can also measure the accuracy of corresponding predictions.
The goal of this book is to present the latest applications of machine learning, mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on. This book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning.
The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides them with a good introduction to many application researches of machine learning, and it is also the source of useful bibliographical information.Machine Learning Methods In The Application Of Speech Emotion Recognition
Automatic Internet Traffic Classification for Early Application Identification
A Greedy Approach for Building Classification Cascades
Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining
Using Learning Automata to Enhance Local-Search Based SAT Solvers with Learning Capability
Comprehensive and Scalable Appraisals of Contemporary Documents
Building an application - generation of ‘items tree’ based on transactional data
Applications of Support Vector Machines in Bioinformatics and Network Security
Machine learning for functional brain mapping
The Application of Fractal Concept to Content-Based Image Retrieval
Gaussian Processes and its Application to the design of Digital Communication Receivers
Adaptive Weighted Morphology Detection Algorithm of Plane Object in Docking Guidance System
Model-based Reinforcement Learning with Model Error and Its Application
Objective-based Reinforcement Learning System for Cooperative Behavior Acquisition
Heuristic Dynamic Programming Nonlinear Optimal Controller
Multi-Scale Modeling and Analysis of Left Ventricular Remodeling Post Myocardial Infarction: Integration of Experimental and Computational Approaches