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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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Machine Learning Applications

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have;

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Domain Adaptation for Visual Understanding

adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel;

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Domain Adaptation for Visual Understanding

adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel;

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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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Regularized System Identification

anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the;

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Alternating Direction Method of Multipliers for Machine Learning

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type;

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Applied Genetic Programming and Machine Learning

evolutionary system. It provides a methodology for integrating GP and machine learning techniques, establishing a robust evolutionary framework for;

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Applied Genetic Programming and Machine Learning

evolutionary system. It provides a methodology for integrating GP and machine learning techniques, establishing a robust evolutionary framework for;

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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 for Planetary Science

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as;

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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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Linac Coherent Light Source (LCLS)

For making a new drug formulation the first and primary step is to choose a promising active pharmaceutical ingredient. Now to detect it;

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Overview of Speech Based Gender Identification

This book focuses on the basics of natural language processing and machine learning required to make a standard speech- based gender;

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Machine Learning

learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning;

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Metaheuristics in Machine Learning

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered;

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Human-in-the-Loop Machine Learning

. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation;

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Wearable Technology for Robotic Manipulation and Learning

vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic;

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Wearable Technology for Robotic Manipulation and Learning

vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic;

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Content-Based Image Classification

information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data;

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Machine Learning and Systems Engineering

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October;

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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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Learning and Decision-Making from Rank Data

, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a;

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Learning and Decision-Making from Rank Data

of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3;

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