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Alternating Direction Method of Multipliers for Machine Learning

of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially;

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Advances in Machine Learning Applications in Software Engineering

establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers direction for;

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Machine Learning for Cloud Management

is the first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds;

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Machine Learning for Cloud Management

is the first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds;

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Self-direction in Adult Learning

a self-direction in learning. it presents strategies for facilitating self-directed learning as an instructional method and for enhancing;

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Self-direction in Adult Learning

a self-direction in learning. it presents strategies for facilitating self-directed learning as an instructional method and for enhancing;

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Deep Learning in Visual Computing

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine;

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Deep Learning in Visual Computing

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine;

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A Novel Machine Learning Method for Preference Identification

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Alternating Projection Methods

This book describes and analyzes all available alternating projection methods for solving the general problem of finding a point in the;

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Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2

Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based;

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Alternating-Current Machine Design

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Machine Learning Control by Symbolic Regression

This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas;

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Thoughtful Machine Learning

can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling;

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling;

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Quantum Machine Learning

learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible;

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Machine Learning in Marketing

play as a research method in the marketing field. The fundamental goal of machine learning is to generalize beyond the examples provided by;

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Evolutionary Approach to Machine Learning and Deep Neural Networks

machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The;

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Evolutionary Approach to Machine Learning and Deep Neural Networks

machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The;

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Algorithmic Aspects of Machine Learning

need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer;

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Algorithmic Aspects of Machine Learning

need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep;

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Development of Two Hybrid Classification Methods for Machine Learning

Nearest Neighbor Methods and Genetic Algorithm" and second study which is named "Utilization of K Nearest Neighbor Method for Expectation;

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Condition Monitoring with Vibration Signals

processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural;

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Domain Adaptation for Visual Understanding

unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to;

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Domain Adaptation for Visual Understanding

unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to;

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