Unstable feature relevance in classification tasks
Published inJyväskylä studies in computing
PublisherUniversity of Jyväskylä
MetadataShow full item record
- Väitöskirjat 
Showing items with similar title or keywords.
Hämäläinen, Joonas (Jyväskylän yliopisto, 2018)Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based ...
Saarela, Mirka; Hämäläinen, Joonas; Kärkkäinen, Tommi (Springer International Publishing, 2017)A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the ...
Prezja, Fabi (2018)In the field of artificial intelligence, supervised machine learning enables us to try to develop automatic recognition systems. In music information retrieval, training and testing such systems is possible with a variety ...
Vehkaoja, Antti; Somppi, Sanni; Törnqvist, Heini; Valldeoriola Cardó, Anna; Kumpulainen, Pekka; Väätäjä, Heli; Majaranta, Päivi; Surakka, Veikko; Kujala, Miiamaaria V.; Vainio, Outi (Elsevier, 2022)Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six ...
Raita-Hakola, Anna-Maria; Pölönen, Ilkka (Copernicus Publications, 2022)The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the ...