title: Analysis of the IJCNN 2011 UTL challenge.
abstract: We organized a challenge in "Unsupervised and Transfer Learning": the UTL challenge (http://clopinet.com/ul). We made available large datasets from various application domains: handwriting recognition, image recognition, video processing, text processing, and ecology. The goal was to learn data representations that capture regularities of an input space for re-use across tasks. The representations were evaluated on supervised learning "target tasks" unknown to the participants. The first phase of the challenge was dedicated to "unsupervised transfer learning" (the competitors were given only unlabeled data). The second phase was dedicated to "cross-task transfer learning" (the competitors were provided with a limited amount of labeled data from "source tasks", distinct from the "target tasks"). The analysis indicates that learned data representations yield significantly better results than those obtained with original data or data preprocessed with standard normalizations and functional transforms.
authors.affiliation: Clopinet, 955 Creston Road, Berkeley, CA 94708, USA. guyon@clopinet.com
authors.forename: Isabelle
authors.initials: I
authors.lastname: Guyon
authors.forename: Gideon
authors.initials: G
authors.lastname: Dror
authors.forename: Vincent
authors.initials: V
authors.lastname: Lemaire
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Silver
authors.forename: Graham
authors.initials: G
authors.lastname: Taylor
authors.forename: David W
authors.initials: DW
authors.lastname: Aha
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D004463:Ecology; D006236:Handwriting; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D015443:Word Processing