Sketching Data Sets for Large-Scale Learning: Keeping only what you need

The ACID-Fission Pay Only for What You Need

Sequential Learning with LS-SVM for Large-Scale Data Sets

We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We imp...

What you see is what you need.

We studied the role of attention and task demands for implicit change detection. Subjects engaged in an object sorting task performed in a virtual reality environment, where we changed the properties of an object while the subject was manipulating it. The task assures that subjects are looking at the changed object immediately before and after the change. Our results demonstrate that in this si...

Sketching Techniques for Large Scale NLP

In this paper, we address the challenges posed by large amounts of text data by exploiting the power of hashing in the context of streaming data. We explore sketch techniques, especially the CountMin Sketch, which approximates the frequency of a word pair in the corpus without explicitly storing the word pairs themselves. We use the idea of a conservative update with the Count-Min Sketch to red...

Find What You Need, Understand What You Find

The developments of the fields of Human-Computer Interaction (HCI) and Information Retrieval (IR) have followed parallel streams with both achieving significant impact in the early part of the 21 st century. The intersection of these two areas engages an active community of researchers who have influenced user interfaces for World Wide Web (WWW) sites and search engines (Marchionini, 2006). The...