Linear classification is a useful tool in machine learning and data mining. For some data in a
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …

With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing
the number of features by removing irrelevant, redundant and noisy data while maintaining …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic
databases. In particular, learning based hashing has received considerable attention due to …

Image hashing is a principled approximate nearest neighbor approach to find similar items
to a query in a large collection of images. Hashing aims to learn a binary-output function that …

Neural networks of ads systems usually take input from multiple resources, eg query-ad
relevance, ad features and user portraits. These inputs are encoded into one-hot or multi-hot …

Nearest neighbor search is a fundamental problem in various research fields like machine
learning, data mining and pattern recognition. Recently, hashing-based approaches, for …

As one of the major search engines in the world, Baidu's Sponsored Search has long
adopted the use of deep neural network (DNN) models for Ads click-through rate (CTR) …

Content-based histopathology image retrieval can be a useful technique for help in
diagnosing various diseases. The process of retrieving images is often time-consuming and …