Asymmetric Heterogeneous Transfer Learning: A Survey

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F17%3A00312664" target="_blank" >RIV/68407700:21240/17:00312664 - isvavai.cz</a>

Result on the web

<a href="http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=mcEq1hvaErY=&t=1" target="_blank" >http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=mcEq1hvaErY=&t=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0006396700170027" target="_blank" >10.5220/0006396700170027</a>

Result language

angličtina

Original language name

Asymmetric Heterogeneous Transfer Learning: A Survey

Original language description

One of the main prerequisites in most machine learning and data mining tasks is that all available data originates from the same domain. In practice, we often can’t meet this requirement due to poor quality, unavailable data or missing data attributes (new task, e.g. cold-start problem). A possible solution can be the combination of data from different domains represented by different feature spaces, which relate to the same task. We can also transfer the knowledge from a different but related task that has been learned already. Such a solution is called transfer learning and it is very helpful in cases where collecting data is expensive, difficult or impossible. This overview focuses on the current progress in the new and unique area of transfer learning - asymmetric heterogeneous transfer learning. This type of transfer learning considers the same task solved using data from different feature spaces. Through suitable mappings between these different feature spaces we can get more data for solving data mining tasks. We discuss approaches and methods for solving this type 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

2017

Confidentiality

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

Article name in the collection

Proceedings of the 6th International Conference on Data Science, Technology and Applications

ISBN

978-989-758-255-4

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

17-27

Publisher name

SciTePress - Science and Technology Publications

Place of publication

Porto

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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