Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and relevance a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose novel efficient regression algorithm, called normalized least-squares (ONLS). perform theoretical analysis by comparing total loss ONLS against gradient desc...

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than fo-cusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmenta-tion as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut ...

We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show th...

We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has several useful effects: there is no need to prenormalize data, the test-time and test-space complexity are reduced, and the algorithms are more robust.

Prothrombin time can be measured roughly on whole blood as in neonates, but is more commonly measured from plasma. Blood is taken into a vacutainer containing liquid citrate. Citrate acts as an anticoagulant by consuming all the calcium in the sample. The blood is mixed and centrifuged to separate blood cells from the plasma. The plasma is placed in a coagulation machine which takes a sample of...

The problem of signal estimation for sparsely and irregularly sampled signals is dealt with using continuous normalized convolution. Image values on real-valued positions are estimated using integration of signals and certainties over a neighbourhood employing a local model of both the signal and the used discrete filters. The result of the approach is that an output sample close to signals wit...

The aim of the present study was to develop a method for predicting the concentration-time profile in humans based on pharmacokinetic data for animals. The method is based on the assumptions that concentration-time profiles of a drug are similar among species and "normalized curves" from a variety of animal species including humans can be superimposed. Normalized curves are obtained by normaliz...

In this paper, we propose the Normalized Freebase Distance (NFD), a new measure for determing semantic concept relatedness that is based on similar principles as the Normalized Web Distance (NWD). We illustrate that the NFD is more effective when comparing ambiguous concepts.