In paper, LANDSAT multispectral image is classified using several unsupervised and supervised techniques. Pixel-by-pixel classification approaches proved to be infeasible as well as time consuming in case of multispectral images. To overcome this, instead of classifying each pixel, feature based classification approach is used. Three supervised techniques namely, k-NN, BPNN and PCNN are investi...

Word-Sense Disambiguation (WSD), holds promise for many NLP applications requiring broad-coverage language understanding, such as summarization (Barzilay and Elhadad, 1997) and question answering (Ramakrishnan et al., 2003). Recent studies have also shown that WSD can benefit machine translation (Vickrey et al., 2005) and information retrieval (Stokoe, 2005). Much work has focused on the comput...

Hashing methods for fast approximate nearest-neighbor search are getting more and more attention with the excessive growth of the available data today. Embedding the points into the Hamming space is an important question of the hashing process. Analogously to machine learning there exist unsupervised, supervised and semi-supervised hashing methods. In this paper we propose a generic procedure t...

Ensembles combine knowledge from distinct machine learning approaches into a general flexible system. While supervised ensembles frequently show great benefit, unsupervised ensembles prove to be more challenging. We propose evaluating various unsupervised ensembles when applied to the unsupervised task of Word Sense Induction with a framework for combining diverse feature spaces and clustering ...

We investigate the effect of corpus size in combining supervised and unsupervised learning for two types of attachment decisions: relative clause attachment and prepositional phrase attachment. The supervised component is Collins’ parser, trained on the Wall Street Journal. The unsupervised component gathers lexical statistics from an unannotated corpus of newswire text. We find that the combin...