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Grammar Based Feature Generation for Time Series Prediction

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation;

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Feature Engineering for Machine Learning and Data Analytics

media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain;

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Feature Engineering for Machine Learning and Data Analytics

data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific;

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Foundations of Time Series Analysis and Prediction Theory

This book provides a mathematical foundation for prediction theory and time series analysis using the idea of regression (prediction) and;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Estimation and Prediction for Certain Models of Spatial Time Series

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Estimation and Prediction for Certain Models of Spatial Time Series

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Machine Learning for Protein Subcellular Localization Prediction

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction;

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Time Series Prediction

range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.;

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Computational Intelligence-based Time Series Analysis

prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a;

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Fuzzy Logic Augmentation of Neural and Optimization Algorithms Theoretical Aspe

application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization;

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Algorithms in Machine Learning Paradigms

applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning;

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Algorithms in Machine Learning Paradigms

applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning;

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Smart Metro Station Systems

, clustering and anomaly detection in crowd hotspots, monitoring and prediction for station humidity, monitoring and spatial prediction for air;

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Prediction of nonlinear nonstationary time series data

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Wavelet-Fractal & Neuro-Fuzzy for Brain Computer Interface Application

selection, multiresolution fractal feature vector (MFFV) associated with genetic algorithm (GA), and neuro-fuzzy time-series prediction to;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

Accurate predictions of storm surge are of importance in many coastal areas in the world to avoid and mitigate its destructive impacts. For;

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Empirical Likelihood and Quantile Methods for Time Series

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g;

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Procedural Content Generation in Games

, grammar-based methods, search-based and evolutionary methods, constraint-based methods, and narrative, terrain, and dungeon generation. The;

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Procedural Content Generation in Games

, grammar-based methods, search-based and evolutionary methods, constraint-based methods, and narrative, terrain, and dungeon generation. The;

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Optimality-Theoretic Syntax: A Declarative Approach

by an examination of assumptions under which the computational tasks of generation and parsing are decidable for an OT-syntactic grammar.;

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Introduction To Time Series Analysis And Forecasting

the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges;

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Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

regard, some limitations of time series modeling are dealt with. For decision-making, innovative distance-based novelty detection techniques are;

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Combustion for Power Generation and Transportation

time series analysis; lasers and optical diagnostics for next generation IC engine development; exergy destruction study on small DI diesel;

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Cambridge Aerospace Series

combustion-based energy conversion systems will continue to be the predominant approach for the majority of our energy usage, and gas turbines will;

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