Representation Learning : Propositionalization and Embeddings / by Nada Lavrač, Vid Podpečan, Marko Robnik-Šikonja.

Format:

Book

Author/Creator:

Lavrač, Nada, author.

Podpečan, Vid, author.

Robnik-Sikonja, Marko, author.

Language:

English

Subjects (All):

Data mining.

Artificial intelligence--Data processing.

Artificial intelligence.

Numerical analysis.

Data Mining and Knowledge Discovery.

Data Science.

Numerical Analysis.

Local Subjects:

Data Mining and Knowledge Discovery.

Data Science.

Numerical Analysis.

Physical Description:

1 online resource (175 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions.

Contents:

Introduction to Representation Learning

Machine Learning Background

Text Embeddings

Propositionalization of Relational Data

Graph and Heterogeneous Network Transformations

Unified Representation Learning Approaches

Many Faces of Representation Learning.

ISBN:

3-030-68817-8

OCLC:

1260343779