Sökning: onr:"swepub:oai:DiVA.org:hj-43236" >
On the behavior of ...
On the behavior of the infinite restricted boltzmann machine for clustering
-
- Huhnstock, Nikolas Alexander, 1988- (författare)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
-
- Karlsson, Alexander (författare)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
-
- Riveiro, Maria, 1978- (författare)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
-
visa fler...
-
- Steinhauer, H. Joe (författare)
- Högskolan i Skövde,Institutionen för informationsteknologi,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
-
visa färre...
-
(creator_code:org_t)
- 2018-04-09
- 2018
- Engelska.
-
Ingår i: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450351911 ; , s. 461-470
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
http://dl.acm.org/ft...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- clustering
- unsupervised
- machine learning
- restricted boltzmann machine
- Skövde Artificial Intelligence Lab (SAIL)
- Skövde Artificial Intelligence Lab (SAIL)
- INF301 Data Science
- INF301 Data Science
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas