https://www.selleckchem.com/JAK.html
We propose two random feature maps for the itemset kernel, which run faster and are more memory efficient than the existing feature map for the itemset kernel. They also generate sparse random features when the original (input) feature vector is sparse and thus linear models using proposed methods . Experiments using real-world datasets demonstrated the effectiveness of the proposed methodology linear models using the proposed random feature maps ran from 10 to 100 times faster than ones based on existing methods.Recognition of ancient Korean-Chines
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