Supervised and Unsupervised Learning for Data Science - Image 1

£72.47

Springer Supervised and Unsupervised Learning for Data Science

Name: Supervised and Unsupervised Learning for Data Science
Brand: Springer
Price: 74.98 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 41 days • 41 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 41 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £72 (23 days, 56.1%)

Price range: £72 - £75

Price levels: 2 different prices over 41 days

Description

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3030224740
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 19 September 2019
Listed Since: 09 May 2019

Barcode

No barcode data available

Springer Supervised and Unsupervised Learning for Data Science

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Supervised and Unsupervised Learning for Data Science

Advances in Data Clustering: Theory and Applications

Statistical Learning and Data Science (Computer Science and Data Analysis)

Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)

Statistical Learning and Data Science (Chapman & Hall/CRC Computer Science & Data Analysis)

Machine Learning Methods

Learning from Imbalanced Data Sets

Machine Learning for Text

Data Science Concepts and Techniques with Applications

Data Science and Big Data Computing: Frameworks and Methodologies

Data Science for Healthcare: Methodologies and Applications

Unsupervised Learning: A Dynamic Approach (IEEE Press Series on Computational Intelligence)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Data Without Labels: Practical Unsupervised Machine Learning

Multimodal Analytics for Next-Generation Big Data Technologies and Applications

Sampling Techniques for Supervised or Unsupervised Tasks (Unsupervised and Semi-Supervised Learning)

The Beginner's Guide to Data Science

Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)

Partitional Clustering via Nonsmooth Optimization: Clustering via Optimization (Unsupervised and Semi-Supervised Learning)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization

Pattern Recognition and Classification: An Introduction

Advances in Big Data Analytics: Theory, Algorithms and Practices

Machine Learning