English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We generated a dataset of 200 GB with 10^9 features, to test our recent b-bitminwise hashing algorithms for training very large-scale logistic regressionand SVM. The results confirm our prior work that, compared with the VW hashingalgorithm (which has the same variance as random projections), b-bit minwisehashing is substantially more accurate at the same storage. For example, withmerely 30 hashed values per data point, b-bit minwise hashing can achievesimilar accuracies as VW with 2^14 hashed values per data point.  We demonstrate that the preprocessing cost of b-bit minwise hashing isroughly on the same order of magnitude as the data loading time. Furthermore,by using a GPU, the preprocessing cost can be reduced to a small fraction ofthe data loading time.  Minwise hashing has been widely used in industry, at least in the context ofsearch. One reason for its popularity is that one can efficiently simulatepermutations by (e.g.,) universal hashing. In other words, there is no need tostore the permutation matrix. In this paper, we empirically verify thispractice, by demonstrating that even using the simplest 2-universal hashingdoes not degrade the learning performance.

Training Logistic Regression and SVM on 200GB Data Using b-Bit Minwise Hashing and Comparisons with Vowpal Wabbit (VW)