An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Data Management Technologies and Applications

ISBN

978-3-319-94809-6

ISSN

—

e-ISSN

1865-0929

Počet stran výsledku

24

Strana od-do

3-26

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Madrid

Datum konání akce

24. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—