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Introduction to Semi-Supervised Learning

and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical;

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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Semi-Supervised Learning

Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;

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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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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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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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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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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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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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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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Structural, Syntactic, and Statistical Pattern Recognition

representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis;

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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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PyTorch Recipes

dynamic graph-based calculations using PyTorch Create PyTorch transformations and graph computations for neural networks Carry out supervised;

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Machine Learning and Big Data

learning with various examples. An empirical study of supervised learning algorithms like Naive Bayes, KNN and semi-supervised learning algorithms;

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Mastering Java Machine Learning

learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning;

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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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Mining the Web

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

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

supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image;

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Deep Learning and Data Labeling for Medical Applications

LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised 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

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

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition;

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Advances in Knowledge Discovery and Data Mining

in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining;

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