Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression da...

This paper explores the relationship between various measures of unsupervised part-of-speech tag induction and the performance of both supervised and unsupervised parsing models trained on induced tags. We find that no standard tagging metrics correlate well with unsupervised parsing performance, and several metrics grounded in information theory have no strong relationship with even supervised...

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. This paper extends SUSTAIN so that it can be used to model unsupervised learning data. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. Two seemingly contradictory unsupervised learning d...

We investigate how unsupervised training of recurrent neural networks (RNNs) and their deep hierarchies can benefit a supervised task like temporal pattern detection. The RNNs are fully and fast trained by unsupervised algorithms and only supervised feed-forward readouts are used. The unsupervised RNNs are shown to perform better in a rigorous comparison against state-of-art random reservoir ne...

1 Knowledge Engineering & Machine Learning Group, Technical University of Catalonia, Barcelona, email: {hnunez, miquel}@lsi.upc.es Abstract. The major hypothesis that we will be prove in this paper is that unsupervised learning techniques of feature weighting are not significantly worse than supervised methods, as is commonly believed in the machine learning community. This paper tests the powe...

* Professor Sungyoung Lee is the corresponding author. Abstract Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer either poor performance of unsupervised learning or lacking of mechanisms to utilize some prior knowledge about data (semi-supervised learning) for improving clustering result. In an effort to archi...