Listing 1 - 10 of 1718 | << page >> |
Sort by
|
Choose an application
Annotation Theoretical and practical aspects of machine learning (ML), algorithms and systems, ML systems involving applications in medicine, biology, industry, manufacturing, security, education, virtual environments, game playing, problem solving and energy.
Choose an application
"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of confidence in the predicted labels of new, unclassifed examples. Confidence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"--
Choose an application
"Invaluable, difficult to find information for the optimum purchase, design, and installation of large-scale, ultra-expensive machine tools, written by an engineer who has been "in the trenches," saving companies millions"--
Choose an application
This first volume of a four volume set, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in machine learning and advanced signal processing theory. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its applicationAscertain how a topic relates to other areas and learn of the research issues yet to be resolvedQuick tutorial reviews of important
Machine elements --- Signal processing. --- Machine learning.
Choose an application
Collection of selected, peer reviewed papers from the 3 rd Asian Pacific Conference on Mechanical Components and Control Engineering (MCCE 2014), September 20-21, 2014, Tianjin, China. The 367 papers are grouped as follows: Chapter 1: Materials Science and Processing Technologies, Chapter 2: General Mechanical Engineering, Applied Mechanics and Dynamics, Chapter 3: Mechatronics and Robotics, Chapter 4: Control Technologies, Automation, Design and Simulation of Manufacturing, Chapter 5: Electrical Engineering and Electric Machines, Chapter 6: Power System and Energy Engineering, its Application
Choose an application
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly
Machine learning. --- Machine learning --- Engineering & Applied Sciences --- Computer Science --- Learning, Machine --- Artificial intelligence --- Machine theory
Choose an application
Annotation This conference provides an idea exchange and discussion platform for researchers and practitioners interested in Intelligent Human Machine and Cybernetics and other topics.
Choose an application
Collection of selected, peer reviewed papers from the 2014 International Conference on Machine Tool Technology and Mechatronics Engineering (ICMTTME 2014), June 22-23, 2014, Guilin, Guangxi, China. The 1440 papers are grouped as follows: Chapter 1: Applied Mechanics, Chapter 6: Communication, Signal and Image Processing, Data Acquisition, Identification and Recognation Technologies Mechanical Engineering, Mechatronics, Automation and Control, Chapter 2: Measurement and Instrumentation, Monitoring, Testing and Detection Technologies, Chapter 3: Numerical Methods, Computation Methods and Algorit
Choose an application
Choose an application
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Listing 1 - 10 of 1718 | << page >> |
Sort by
|