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Advanced Supervised and Semi-supervised Learning
0 - Default Title
Description
In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge.
This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.
Product details
Number of Pages:
328
Release Date:
2025-10-17
Publication Date:
2025-10-17
Publisher:
Springer
Languages:
Original:
English
ISBN10:
3031999274
ISBN13:
9783031999277
GPSR Manufacturer Reference:
Weight:
656 g
Height:
160 cm
Width:
241 cm
Thickness:
24 cm
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