Download or read book Discriminative Locally Adaptive Nearest Centroid Classifier written by Yong-Peng Sun and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic speech recognition (ASR) is a forefront of technology and research today. The effectiveness of ASR depends upon the accurate and quick classification of phonemes, which are the basic building blocks of speech. To derive such a classifier for phoneme classification in the context of ASR is the subject of my MASc thesis at the University of Waterloo carried out in between April 2011 and July 2012 under the supervision of Professor Fakhreddine Karray. Drawing upon several recent research topics applied to this area, such as discriminative learning and locally adaptive metrics, a novel classifier referred to as the discriminative locally-adaptive nearest centroid classifier (DLANC). DLANC is structurally simple, very quick to train on even very large sets of data, and it also produces very good classification results on standard TIMIT data. This book describes the DLANC classifier in detail, including its background and how it is derived. A detailed comparison between the DLANC classifier and several other existing classifiers for phoneme classification are made on standard TIMIT data. Numerous illustrations and diagrams make many theoretical points easy to understand.
Download or read book A Discriminative Locally adaptive Nearest Centroid Classifier for Phoneme Classification written by Yong-Peng Sun and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Phoneme classification is a key area of speech recognition. Phonemes are the basic modeling units in modern speech recognition and they are the constructive units of words. Thus, being able to quickly and accurately classify phonemes that are input to a speech-recognition system is a basic and important step towards improving and eventually perfecting speech recognition as a whole. Many classification approaches currently exist that can be applied to the task of classifying phonemes. These techniques range from simple ones such as the nearest centroid classifier to complex ones such as support vector machine. Amongst the existing classifiers, the simpler ones tend to be quicker to train but have lower accuracy, whereas the more complex ones tend to be higher in accuracy but are slower to train. Because phoneme classification involves very large datasets, it is desirable to have classifiers that are both quick to train and are high in accuracy. The formulation of such classifiers is still an active ongoing research topic in phoneme classification. One paradigm in formulating such classifiers attempts to increase the accuracies of the simpler classifiers with minimal sacrifice to their running times. The opposite paradigm attempts to increase the training speeds of the more complex classifiers with minimal sacrifice to their accuracies. The objective of this research is to develop a new centroid-based classifier that builds upon the simpler nearest centroid classifier by incorporating a new discriminative locally-adaptive training procedure developed from recent advances in machine learning. This new classifier, which is referred to as the discriminative locally-adaptive nearest centroid (DLANC) classifier, achieves much higher accuracies as compared to the nearest centroid classifier whilst having a relatively low computational complexity and being able to scale up to very large datasets.
Download or read book Discriminant Adaptive Nearest Neighbor Classification written by Stanford University. Department of Statistics and published by . This book was released on 1994 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Locally Adaptive Techniques for Pattern Classification written by Carlotta Domeniconi and published by . This book was released on 2002 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Frontier Computing written by Jason C Hung and published by Springer. This book was released on 2016-07-28 with total page 1207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the 4th International Conference on Frontier Computing (FC 2015), Bangkok, Thailand, September 9-11, 2015, and brings together state-of-the-art results covering many aspects of emerging computer science and information technology from international academic and industrial researchers. FC 2015 aimed at providing an open forum to reach a comprehensive understanding of the recent advances and developing trends in information technology, computer science and engineering, with themes under the scope of communication networks, business intelligence and knowledge management, web intelligence, and any related fields that prompt the development of information technology. Contributions cover a wide spectrum of topics: database and data mining, networking and communications, web and internet of things, embedded system, soft computing, social network analysis, security and privacy, optics communication, and ubiquitous/pervasive computing. Many papers have shown great academic potential and value, and in addition indicate promising directions of research in the focused realm of this conference series. Readers, including students, researchers, and industry professionals, will benefit from the results presented in this book, and it provides indicators for emerging trends for those starting their research careers.
Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by Newnes. This book was released on 1999 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an introduction to statistical pattern recognition theory and techniques. Most of the material presented in this book is concerned with discrimination and classification and has been drawn from a wide range of literature including that of engineering, statistics, computer science and the social sciences. This book is an attempt to provide a concise volume containing descriptions of many of the most useful of today's pattern processing techniques including many of the recent advances in nonparametric approaches to discrimination developed in the statistics literature and elsewhere. The techniques are illustrated with examples of real-world applications studies. Pointers are also provided to the diverse literature base where further details on applications, comparative studies and theoretical developments may be obtained"--Page [xv].
Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
Download or read book Advanced Data Mining and Applications written by Longbing Cao and published by Springer. This book was released on 2010-11-18 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
Download or read book Data Classification written by Charu C. Aggarwal and published by CRC Press. This book was released on 2014-07-25 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods-The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains-The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations-The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.
Download or read book Advanced Data Mining and Applications written by Longbing Cao and published by Springer. This book was released on 2010-11-18 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.
Download or read book Artificial Intelligence Based Brain Computer Interface written by Varun Bajaj and published by Academic Press. This book was released on 2022-02-04 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for the modeling of non-invasive modalities of medical signals such as EEG, MRI and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. The book emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI. Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in the management of chronic conditions, databases, and in the delivery of health services. Provides readers with an understanding of key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for the early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others Includes illustrative examples on how Artificial Intelligence can be applied to the Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders
Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.
Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2024-01-25 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.
Download or read book Computer Vision Technology for Food Quality Evaluation written by Da-Wen Sun and published by Academic Press. This book was released on 2016-04-07 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision Technology for Food Quality Evaluation, Second Edition continues to be a valuable resource to engineers, researchers, and technologists in research and development, as well as a complete reference to students interested in this rapidly expanding field. This new edition highlights the most recent developments in imaging processing and analysis techniques and methodology, captures cutting-edge developments in computer vision technology, and pinpoints future trends in research and development for food quality and safety evaluation and control. It is a unique reference that provides a deep understanding of the issues of data acquisition and image analysis and offers techniques to solve problems and further develop efficient methods for food quality assessment. Thoroughly explains what computer vision technology is, what it can do, and how to apply it for food quality evaluation Includes a wide variety of computer vision techniques and applications to evaluate a wide variety of foods Describes the pros and cons of different techniques for quality evaluation
Download or read book Pattern Recognition And Big Data written by Sankar Kumar Pal and published by World Scientific. This book was released on 2016-12-15 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
