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Showing 1 to 5 of 5 for “"large-scale classification"”.
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Selective algorithms for large-scale classification and structured learning
Made available in DSpace on 2015-07-22T22:17:14Z (GMT). No. of bitstreams: 2 CHANG-DISSERTATION-2015.pdf: 1718796 bytes, checksum: acb2f0fcac6237b94e5733d5534763cb (MD5) LICENSE.txt: 4210 bytes, checksum: 781f641005bc15b659d6f1cee238b5c2 (MD5) Previous issue date: 2015-04-23
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Error-correcting codes and applications to large scale classification systems
… Using distributed output coding, we were able to scale a neural-network-based algorithm to handle nearly 10,000 output classes. In particular, we built a prototype OCR engine for Devanagari and Korean texts based upon distributed output coding. We found that the resulting classifiers performed …
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Data Mining via Support Vector Machines: Scalability, Applicability, and Interpretability
… data mining problems---(1) classifying with large data sets, (2) classifying without negative data (i.e., single-class classification), and (3) discovering discriminant feature combinations---and presents solutions that are based on a principled methodology, i.e., Support Vector Machines …
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On the classification of time series and cross wavelet phase variance
… features of time series data. Its application in large time series classification experiments, however, has been severely limited due to the large amount of redundant associated information. By extending the capabilities of the CWT to perform cross wavelet analysis (CWA), common frequency …
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, …