Jo, T: Word Clustering

Jo, Prof Taeho

ISBN 13: 9798345260074

Editore: Amazon Digital Services LLC - Kdp, 2024

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Neuware - This book is concerned with graph-based machine learning algorithms which are applied to word clustering. The book is authored by collecting slides about the application of machine learning algorithms to word clustering; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to word clustering and is composed of five sections. Word clustering is defined as the process of segmenting a word group into subgroups each of which contains semantically similar words; in this book, the scope is restricted to only semantic word clustering. This book covers graph based modified versions of AHC Algorithm, K means algorithm, Linear Online Clustering Algorithm, and Divisive Algorithm, as approaches to word clustering.

Codice articolo 9798345260074

Dati bibliografici

• Titolo: Jo, T: Word Clustering
• Editore: Amazon Digital Services LLC - Kdp
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 13: 9798345260074
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 229 L x 152 A x 4 P (millimetri) 229 millimetri di larghezza per 152 millimetri di altezza per 4 millimetri di profondità
• Peso: 104 grammi

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Riassunto

This book is concerned with graph-based machine learning algorithms which are applied to word clustering. The book is authored by collecting slides about the application of machine learning algorithms to word clustering; each page is given as a slide. This book is intended for ones who study applications of machine learning algorithms to word clustering and is composed of five sections. Word clustering is defined as the process of segmenting a word group into subgroups each of which contains semantically similar words; in this book, the scope is restricted to only semantic word clustering. This book covers graph based modified versions of AHC Algorithm, K means algorithm, Linear Online Clustering Algorithm, and Divisive Algorithm, as approaches to word clustering.