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Advanced State Space Methods for Neural and Clinical Data

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data;

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault;

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EEG/ERP Analysis

consists of 13 chapters and includes the advanced techniques used for signal enhancement, source localization, data fusion, classification, and;

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EEG/ERP Analysis

consists of 13 chapters and includes the advanced techniques used for signal enhancement, source localization, data fusion, classification, and;

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Hydrological Data Driven Modelling

that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with;

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Introduction to Deep Learning for Healthcare

multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for;

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Automated Deep Learning Using Neural Network Intelligence

to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Advanced Analysis of Gene Expression Microarray Data

This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for;

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State of the Art in Neural Networks and Their Applications

image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case;

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Advanced Methods of Biomedical Signal Processing

techniques, such as multisource and multiscale integration of information for physiology and clinical decision; the impact of advanced methods;

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Data Mining

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge;

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Time Series Modeling of Neuroscience Data

applications to signal detection problems in EEG data analysis An innovation-based method for the characterization of nonlinear and/or non-Gaussian;

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Cambridge Studies in Mathematical Biology Introduction to Theoretical Neurobiology

of the book discusses stochastic models of neural activity and ends with a statistical analysis of neuronal data with emphasis on spike trains;

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Cambridge Studies in Mathematical Biology Introduction to Theoretical Neurobiology

of the book discusses stochastic models of neural activity and ends with a statistical analysis of neuronal data with emphasis on spike trains;

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Introduction to Theoretical Neurobiology

of the book discusses stochastic models of neural activity and ends with a statistical analysis of neuronal data with emphasis on spike trains;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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Time Series Analysis for the State Space Model with R Stan

combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given;

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Time Series Analysis for the State Space Model with R Stan

combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given;

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Machine Learning in Medicine

statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis;

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Kernels For Structured Data

structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and;

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Joint Models of Neural and Behavioral Data

This book presents a flexible Bayesian framework for combining neural and cognitive models. Traditionally, studies in cognition and;

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Neurocomputation in Remote Sensing Data Analysis

growing application needs for high precision spatial products, need to be interpreted in shorter times and with increasing accuracy. Neural;

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Laser Scanning Systems in Highway and Safety Assessment: Analysis of Highway Geometry and Safety Using Lidar

comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for;

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Mastering Predictive Analytics with scikit-learn and TensorFlow

and regression problems Who this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software;

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State-Space Methods for Time Series Analysis

likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also;

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State-Space Methods for Time Series Analysis

likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also;

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