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Introduction to Semi-Supervised Learning

labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and;

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Supervised and Unsupervised Learning for Data Science

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Semi-Supervised Learning

examples are logically classified from simple to difficult. In the eighth chapter, Tang combines semi-supervised learning with geo-tagged photo;

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Machine Learning Foundations

core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and;

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Semi-Supervised Dependency Parsing

This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular;

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Mastering Machine Learning Algorithms

data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement;

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Beginning Anomaly Detection Using Python-Based Deep Learning

and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly;

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An Introduction to Variational Autoencoders

wide array of applications from generative modeling, semi-supervised learning to representation learning.The authors expand earlier work and;

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Applied Machine Learning for Smart Data Analysis

unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections;

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Robust Representation for Data Analytics

unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations;

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Pattern Recognition

in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. Thoroughly developed to include;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible;

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Ensemble Methods

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms;

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Distributionally Robust Learning

Many of the modern techniques to solve supervised learning problems suffer from a lack of interpretability and analyzability that do not;

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Mining the Web

, hyperlink analysis, and supervised and semi-supervised learning.* Analyzes current applications for resource discovery and social network;

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Mastering Machine Learning Algorithms

machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised;

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Scaling up Machine Learning

, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and;

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Scaling up Machine Learning

, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and;

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Artificial Intelligence and Industrial Applications

learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The;

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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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Graph-Based Semi-Supervised Learning

this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed;

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Machine Learning

selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and;

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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance;

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The The Supervised Learning Workshop

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key Features;

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Supervised Reinforcement Learning

Supervised Reinforcement Learning is een boek van Karla Conn;

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