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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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Recent Advances In Stochastic Modeling And Data Analysis

in different fields is emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural;

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Applications of Artificial Neural Networks for Nonlinear Data

that can properly handle this form of data is artificial neural networks. These modeling systems present a high level of benefits in analyzing;

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Case Studies in Neural Data Analysis

A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural;

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Time Series Databases - New Ways to Store and Acces Data

Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you;

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Statistical Learning for Big Dependent Data

large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data;

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Neural Information Processing Part I

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part II

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part III

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Neural Information Processing Part IV

selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data;

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Advanced State Space Methods for Neural and Clinical Data

in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Time Series Modeling of Neuroscience Data

show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Information Processing

Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series;

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Neural Networks for Applied Sciences and Engineering

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering;

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Time Series Analysis

details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets;

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Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

Get better insights from time-series data and become proficient in model performance analysis Key Features Explore popular and modern;

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Analyzing Ecological Data

-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series;

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Analyzing Ecological Data

-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series;

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Neural Information Processing

from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based;

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Analyzing Neural Time Series Data

A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data;

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