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Supervised and Unsupervised Pattern Recognition

and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel;

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

The two-volume set LNCS 8258 and 8259 constitutes the refereed proceedings of the 18th Iberoamerican Congress on Pattern Recognition, CIARP;

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Similarity-Based Pattern Recognition

This book constitutes the proceedings of the Second International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD;

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Textbook on Pattern Recognition

of supervised, unsupervised and reinforces learning to build a complete background of professionals and engineering students. Every pattern of the image;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling;

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Advantages and Pitfalls of Pattern Recognition

the data of multivariate analysis. This book explores both supervised and unsupervised pattern recognition techniques, while providing insight;

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Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

recognition algorithms and methodologies including but not limited to; supervised and unsupervised classification algorithms, Ensemble learning;

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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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Chemometrics

Supervised and unsupervised pattern recognition Classical and inverse methods of multivariate calibration Complete with helpful chapter-end;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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Structural, Syntactic, and Statistical Pattern Recognition

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition;

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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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Pattern Recognition

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to;

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Similarity Based Pattern Recognition

This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD;

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TensorFlow 2.0 Quick Start Guide

traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style;

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Unsupervised Learning Algorithms

of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained;

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Time Series Clustering and Classification

Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including;

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Time Series Clustering and Classification

. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide;

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Congress on Intelligent Systems

classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles;

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Congress on Intelligent Systems

classification, soft computing for machine vision, pattern recognition, human computer interaction, robotic devices and systems, autonomous vehicles;

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Semi-Supervised and Unsupervised Machine Learning

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised;

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Machine Learning with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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HandsOn Unsupervised Learning Using Python

different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end;

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Machine Learning Fundamentals

Understand the difference between supervised and unsupervised models Learn the usage of machine learning through real-world examples Book;

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM;

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Perspectives on Pattern Recognition

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories;

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