Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Rahul Khanna, Mariette Awad
Format: PDF / Kindle (mobi) / ePub
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.
Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.
Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms.
Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
Developing robust yet computationally efficient algorithms is still a challenging problem, given the increased awareness of energy-aware computing. Offline sensing occurs by optically scanning words and then transforming those images to letter code usable in the computer software environment. Online recognition automatically converts the writing on a graphics tablet or pen-based computer screen into letter code. HWR systems can also be classified as writer dependent or writer independent, with
distribution of the model parameters (which is Gaussian) is as shown in Equation 4-26, with the mean computed in Equation 4-27, and the standard deviation scale factor, in Equation 4-28. The mean is simply the Moore-Penrose pseudoinverse of the predictive variables multiplied by the observations. Given some observations, the posterior probability of the model variance is computed, and an inverse chi-squared distribution (see Equation 4-29), with n - k degrees of freedom and a scale factor s2 (see
29, no. 6 (2012): 82–97. Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation 18, no. 7 (2006): 1527–1554. Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. “Reducing the Dimensionality of Data with Neural Networks.” Science 313, no. 5786 (2006): 504–507. 144 Chapter 7 ■ Deep Neural Networks Hochreiter, Sepp. “Untersuchungen zu dynamischen neuronalen Netzen.” Master's thesis, Technical University of Munich, 1991.
connections to other cells in the same region, which is used to recognize the state of the network at some point in time. Cells can predict when they will become active by looking at their connections. A particular cell may be part of dozens or hundreds of temporal transitions. Therefore, every cell has several dendrite segments, not just one. There are three phases involved with temporal pooling. In the first phase each cell’s active state is computed. Phase 2 computes each cell’s predictive
“Mining Frequent Patterns without Candidate Generation.” In SIGMOD/PODS ’00: ACM international Conference on Management of Data and Symposium on Principles of Database Systems, Dallas, TX, USA, May 15–18, 2000, edited by Weidong Chen, Jeffrey Naughton, Philip A. Bernstein. New York: ACM (2000): 1–12. Jang, J.-S. R. “ANFIS: Adaptive-Network-Based Fuzzy Inference System.” IEEE Transactions on Systems, Man and Cybernetics 23, no. 3 (1993): 665–685. Kohavi, Ron, and Foster Provost. “Glossary of