An experiment is carried out on a subset of a first year group in Semester 1, 2001 for five separate fiftyminute sessions. Five groups are taught using one teaching technique based around the idea of passive learning. Five further groups are taught using a second technique based on active learning within small groups. The remaining three groups face a third approach based on a combination of th...

We propose an appearance manifold with view-dependent covariance matrix for face recognition from video sequences in two learning frameworks: the supervised-learning and the incremental unsupervisedlearning. The advantages of this method are, first, the appearance manifold with view-dependent covariance matrix model is robust to pose changes and is also noise invariant, since the embedded covar...

Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method ...

Speech recognition systems have achieved high recognition performance for several tasks. However, the performance of such systems is dependent on the tremendously costly development work of preparing vast amounts of task-matched transcribed speech data for supervised training. The key problem here is the cost of transcribing speech data. The cost is repeatedly required to support new languages ...

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A new unsupervised feature selection method, i.e., Robust Unsupervised Feature Selection (RUFS), is proposed. Unlike traditional unsupervised feature selection methods, pseudo cluster labels are learned via local learning regularized robust nonnegative matrix factorization. During the label learning process, feature selection is performed simultaneously by robust joint l2,1 norms minimization. ...

Brown clustering is an established technique, used in hundreds of computational linguistics papers each year, to group word types that have similar distributional information. It is unsupervised and can be used to create powerful word representations for machine learning. Despite its improbable success relative to more complex methods, few have investigated whether Brown clustering has really b...

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...