Learning on Small Amounts of Labelled Data

Student: Kolupaev Ilia

Supervisor: Sergey Lisitsyn

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2018

Despite ubiquitous availability of potentially useful data, raw data often comes unlabelled. On the other hand, the majority of research and business uses of machine learning are based on supervised learning, which requires large amount of labelled data and consequently demands manual labeling. This research reviews approaches for solving this problem, analyzes their properties, advantages and disadvantages, and considers possible applications for them.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses