Learning Theory And Kernel Machines
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Learning Theory And Kernel Machines
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Author : Bernhard Schoelkopf
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-08-11
Learning Theory And Kernel Machines written by Bernhard Schoelkopf and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-11 with Computers categories.
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Learning Theory And Kernel Machines
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Author : Bernhard Scholkopf
language : en
Publisher:
Release Date : 2014-01-15
Learning Theory And Kernel Machines written by Bernhard Scholkopf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Machine Learning Theory And Applications
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Author : Xavier Vasques
language : en
Publisher: John Wiley & Sons
Release Date : 2024-01-31
Machine Learning Theory And Applications written by Xavier Vasques and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
Learning Theory And Kernel Machines
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Author :
language : en
Publisher:
Release Date : 2003
Learning Theory And Kernel Machines written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Progress In Pattern Recognition Image Analysis And Applications
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Author : Alberto Sanfeliu
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-10-15
Progress In Pattern Recognition Image Analysis And Applications written by Alberto Sanfeliu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-15 with Computers categories.
First of all, we want to congratulate two new research communities from M- ico and Brazil that have recently joined the Iberoamerican community and the International Association for Pattern Recognition. We believe that the series of congresses that started as the “Taller Iberoamericano de Reconocimiento de Patrones (TIARP)”, and later became the “Iberoamerican Congress on Pattern Recognition (CIARP)”, has contributed to these groupconsolidatione?orts. We hope that in the near future all the Iberoamerican countries will have their own groups and associations to promote our areas of interest; and that these congresses will serve as the forum for scienti?c research exchange, sharing of - pertise and new knowledge, and establishing contacts that improve cooperation between research groups in pattern recognition and related areas. CIARP 2004 (9th Iberoamerican Congress on Pattern Recognition) was the ninthinaseriesofpioneeringcongressesonpatternrecognitionintheIberoam- ican community. As in the previous year, CIARP 2004 also included worldwide participation. It took place in Puebla, Mexico. The aim of the congress was to promote and disseminate ongoing research and mathematical methods for pattern recognition, image analysis, and applications in such diverse areas as computer vision, robotics, industry, health, entertainment, space exploration, telecommunications, data mining, document analysis,and natural languagep- cessing and recognition, to name a few.
Bio Kernel Machines And Applications
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Author : Zheng Rong Yang
language : en
Publisher: World Scientific
Release Date : 2024-03-06
Bio Kernel Machines And Applications written by Zheng Rong Yang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-06 with Computers categories.
Due to its capability of handling very complex problems and its high flexibility in adapting to different algorithms, the kernel machine plays a crucial role in machine learning.Bio-Kernel Machines and Applications will introduce a new type of kernel machine for the exploration and modeling between the genotypic inherent structures of short protein sequences or nucleic sequences and the phenotypic biological properties or functions of proteins or nucleotides.The book seeks to establish the fundamentals of the bio-kernel machines by presenting the basic principle and theory of the kernel machine and the various formats of kernel machines, such as string kernel machines adapted for biological applications. The book will also introduce several biological applications of the mutation matrices, demonstrating how mutation matrices can enhance the efficiency and biological relevance of machine learning models applied in specific biological problems.Through analyzing current applications of bio-kernel machines, readers will delve into the advantages of the bio-kernel machines and explore how bio-kernel machines can be further enhanced to tackle a wide spectrum of biological challenges and pave the way for future advancements.
Bridging The Gap Between Graph Edit Distance And Kernel Machines
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Author : Michel Neuhaus
language : en
Publisher: World Scientific
Release Date : 2007-09-03
Bridging The Gap Between Graph Edit Distance And Kernel Machines written by Michel Neuhaus and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-03 with Computers categories.
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain — commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions
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Author : Igor V. Tetko
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Artificial Neural Networks And Machine Learning Icann 2019 Workshop And Special Sessions written by Igor V. Tetko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Large Scale Kernel Machines
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Author : Léon Bottou
language : en
Publisher: MIT Press
Release Date : 2007
Large Scale Kernel Machines written by Léon Bottou and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically. Contributors Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov
Digital Signal Processing With Kernel Methods
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Author : Jose Luis Rojo-Alvarez
language : en
Publisher: John Wiley & Sons
Release Date : 2018-02-05
Digital Signal Processing With Kernel Methods written by Jose Luis Rojo-Alvarez and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-05 with Technology & Engineering categories.
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.