Kernel Based Machine Learning Framework For Neural Decoding

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Kernel Based Machine Learning Framework for Neural Decoding

Author : Lin Li
Publisher :
ISBN 13 :
Total Pages : 128 pages
Book Rating : 4.:/5 (869 download)

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Book Synopsis Kernel Based Machine Learning Framework for Neural Decoding by : Lin Li

Download or read book Kernel Based Machine Learning Framework for Neural Decoding written by Lin Li and published by . This book was released on 2012 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Secondly, the precise control of the firing pattern in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. In this work, we propose a multiple-input-multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a RKHS. The control scheme uses an inverse controller to approximate the neural circuit's inverse. The proposed control system takes advantage of the precise timing of the neural events using the Schoenberg kernel based decoding methodology we proposed before. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system output and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel can successfully drive the elicited responses close to the original target responses even when significant perturbations occur.

Kernel-based Machine Learning Methods for Neural Brain-machine Interfaces

Author : Lavi Shpigelman
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (713 download)

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Book Synopsis Kernel-based Machine Learning Methods for Neural Brain-machine Interfaces by : Lavi Shpigelman

Download or read book Kernel-based Machine Learning Methods for Neural Brain-machine Interfaces written by Lavi Shpigelman and published by . This book was released on 2009 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:

A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding

Author : Michael C. Burkhart
Publisher : ProQuest Dissertations Publishing
ISBN 13 :
Total Pages : 134 pages
Book Rating : 4.6/5 (647 download)

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Book Synopsis A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding by : Michael C. Burkhart

Download or read book A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding written by Michael C. Burkhart and published by ProQuest Dissertations Publishing. This book was released on 2019-05-26 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior distribution of the current state given all measurements up to the present time. For example, the Apollo lunar module implemented a Kalman filter to infer its location from a sequence of earth-based radar measurements and land safely on the moon. To perform Bayesian filtering, we require a measurement model that describes the conditional distribution of each observation given state. The Kalman filter takes this measurement model to be linear, Gaussian. Here we show how a nonlinear, Gaussian approximation to the distribution of state given observation can be used in conjunction with Bayes’ rule to build a nonlinear, non-Gaussian measurement model. The resulting approach, called the Discriminative Kalman Filter (DKF), retains fast closed-form updates for the posterior. We argue there are many cases where the distribution of state given measurement is better-approximated as Gaussian, especially when the dimensionality of measurements far exceeds that of states and the Bernstein—von Mises theorem applies. Online neural decoding for brain-computer interfaces provides a motivating example, where filtering incorporates increasingly detailed measurements of neural activity to provide users control over external devices. Within the BrainGate2 clinical trial, the DKF successfully enabled three volunteers with quadriplegia to control an on-screen cursor in real-time using mental imagery alone. Participant “T9” used the DKF to type out messages on a tablet PC. Nonstationarities, or changes to the statistical relationship between states and measurements that occur after model training, pose a significant challenge to effective filtering. In brain-computer interfaces, one common type of nonstationarity results from wonkiness or dropout of a single neuron. We show how a robust measurement model can be used within the DKF framework to effectively ignore large changes in the behavior of a single neuron. At BrainGate2, a successful online human neural decoding experiment validated this approach against the commonly-used Kalman filter.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Kernel Methods and Machine Learning

Author : S. Y. Kung
Publisher : Cambridge University Press
ISBN 13 : 110702496X
Total Pages : 617 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Kernel Methods and Machine Learning by : S. Y. Kung

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Composing Fisher Kernels from Deep Neural Models

Author : Tayyaba Azim
Publisher : Springer
ISBN 13 : 3319985248
Total Pages : 59 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Composing Fisher Kernels from Deep Neural Models by : Tayyaba Azim

Download or read book Composing Fisher Kernels from Deep Neural Models written by Tayyaba Azim and published by Springer. This book was released on 2018-08-23 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fisher vectors using feature selection and compression techniques. Feature selection and feature compression are two of the most popular off-the-shelf methods for reducing data’s high-dimensional memory footprint and thus making it suitable for large-scale visual retrieval and classification. Kernel methods long remained the de facto standard for solving large-scale object classification tasks using low-level features, until the revival of deep models in 2006. Later, they made a comeback with improved Fisher vectors in 2010. However, their supremacy was always challenged by various versions of deep models, now considered to be the state of the art for solving various machine learning and computer vision tasks. Although the two research paradigms differ significantly, the excellent performance of Fisher kernels on the Image Net large-scale object classification dataset has caught the attention of numerous kernel practitioners, and many have drawn parallels between the two frameworks for improving the empirical performance on benchmark classification tasks. Exploring concrete examples on different data sets, the book compares the computational and statistical aspects of different dimensionality reduction approaches and identifies metrics to show which approach is superior to the other for Fisher vector encodings. It also provides references to some of the most useful resources that could provide practitioners and machine learning enthusiasts a quick start for learning and implementing a variety of deep learning models and kernel functions.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Practical Machine Learning with Rust

Author : Joydeep Bhattacharjee
Publisher : Apress
ISBN 13 : 1484251210
Total Pages : 362 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Machine Learning with Rust by : Joydeep Bhattacharjee

Download or read book Practical Machine Learning with Rust written by Joydeep Bhattacharjee and published by Apress. This book was released on 2019-12-10 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Learning with Kernels

Author : Bernhard Scholkopf
Publisher : National Geographic Books
ISBN 13 : 0262536579
Total Pages : 0 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Learning with Kernels by : Bernhard Scholkopf

Download or read book Learning with Kernels written by Bernhard Scholkopf and published by National Geographic Books. This book was released on 2018-06-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Kernel Methods for Pattern Analysis

Author :
Publisher :
ISBN 13 : 9780511214189
Total Pages : 462 pages
Book Rating : 4.2/5 (141 download)

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Book Synopsis Kernel Methods for Pattern Analysis by :

Download or read book Kernel Methods for Pattern Analysis written by and published by . This book was released on 2004 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: The kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.

Advances in Neural Computation, Machine Learning, and Cognitive Research VI

Author : Boris Kryzhanovsky
Publisher : Springer Nature
ISBN 13 : 3031190327
Total Pages : 585 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Advances in Neural Computation, Machine Learning, and Cognitive Research VI by : Boris Kryzhanovsky

Download or read book Advances in Neural Computation, Machine Learning, and Cognitive Research VI written by Boris Kryzhanovsky and published by Springer Nature. This book was released on 2022-10-18 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIV International Conference on Neuroinformatics, held on October 17–21, 2022, in Moscow, Russia.

Large-Scale Kernel Machines

Author : L|̈on Bottou
Publisher :
ISBN 13 : 9780262292696
Total Pages : 409 pages
Book Rating : 4.2/5 (926 download)

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Book Synopsis Large-Scale Kernel Machines by : L|̈on Bottou

Download or read book Large-Scale Kernel Machines written by L|̈on Bottou and published by . This book was released on 2016 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Neural Information Processing Systems 15

Author : Suzanna Becker
Publisher : MIT Press
ISBN 13 : 9780262025508
Total Pages : 1738 pages
Book Rating : 4.0/5 (255 download)

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Book Synopsis Advances in Neural Information Processing Systems 15 by : Suzanna Becker

Download or read book Advances in Neural Information Processing Systems 15 written by Suzanna Becker and published by MIT Press. This book was released on 2003 with total page 1738 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the 2002 Neural Information Processing Systems Conference.

The Use of Kernel-based Machine Learning Algorithms in Virtual Screening

Author : David John Wood
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (55 download)

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Book Synopsis The Use of Kernel-based Machine Learning Algorithms in Virtual Screening by : David John Wood

Download or read book The Use of Kernel-based Machine Learning Algorithms in Virtual Screening written by David John Wood and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

EEG Signal Processing and Feature Extraction

Author : Li Hu
Publisher : Springer Nature
ISBN 13 : 9811391130
Total Pages : 437 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis EEG Signal Processing and Feature Extraction by : Li Hu

Download or read book EEG Signal Processing and Feature Extraction written by Li Hu and published by Springer Nature. This book was released on 2019-10-12 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.

Advanced Methods in Biomedical Signal Processing and Analysis

Author : Kunal Pal
Publisher : Academic Press
ISBN 13 : 0323859542
Total Pages : 434 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Advanced Methods in Biomedical Signal Processing and Analysis by : Kunal Pal

Download or read book Advanced Methods in Biomedical Signal Processing and Analysis written by Kunal Pal and published by Academic Press. This book was released on 2022-09-07 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques. Gives advanced methods in signal processing Includes machine and deep learning methods Presents experimental case studies

Dive Into Deep Learning

Author : Joanne Quinn
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.