An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00322508" target="_blank" >RIV/68407700:21240/18:00322508 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-94809-6_1#citeas" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-94809-6_1#citeas</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-94809-6_1" target="_blank" >10.1007/978-3-319-94809-6_1</a>

Result language

angličtina

Original language name

An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches

Original language description

In practice we often encounter classification tasks. In order to solve these tasks, we need a sufficient amount of quality data for the construction of an accurate classification model. However, in some cases, the collection of quality data poses a demanding challenge in terms of time and finances. For example in the medical area, we encounter lack of data about patients. Transfer learning introduces the idea that a possible solution can be combining data from different domains represented by different feature spaces relating to the same task. We can also transfer knowledge from a different but related task that has been learned already. This overview focuses on the current progress in the novel area of asymmetric heterogeneous transfer learning. We discuss approaches and methods for solving these types of transfer learning tasks. Furthermore, we mention the most used metrics and the possibility of using metric or similarity learning.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Data Management Technologies and Applications

ISBN

978-3-319-94809-6

ISSN

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e-ISSN

1865-0929

Number of pages

24

Pages from-to

3-26

Publisher name

Springer International Publishing

Place of publication

Cham

Event location

Madrid

Event date

Jul 24, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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