AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Support vector machines for histogram-based image classification

Abstract: Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y)=e/sup -/spl rho///spl Sigma//sub i//sup |xia-yia|b/ with a /spl les/1 and b/spl les/2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x/sub i//spl rarr/x/sub i//sup a/ improves the performance of linear SVM to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.

The anatomy of a context-aware application

Abstract: We describe a sensor-driven, or sentient, platform for context-aware computing that enables applications to follow mobile users as they move around a building. The platform is particularly suitable for richly equipped, networked environments. The only item a user is required to carry is a small sensor tag, which identifies them to the system and locates them accurately in three dimensions. The platform builds a dynamic model of the environment using these location sensors and resource information gathered by telemetry software, and presents it in a form suitable for application programmers. Use of the platform is illustrated through a practical example, which allows a user's current working desktop to follow them as they move around the environment.

A quasi-orthogonal space-time block code

Abstract: It has been shown that a complex orthogonal design that provides full diversity and full transmission rate for a space-time block code is not possible for more than two antennas. Previous attempts have been concentrated in generalizing orthogonal designs which provide space-time block codes with full diversity and a high transmission rate. We design rate one codes which are quasi-orthogonal and provide partial diversity. The decoder of the proposed codes works with pairs of transmitted symbols instead of single symbols.

Mining with rarity: a unifying framework

Abstract: Rare objects are often of great interest and great value Until recently, however, rarity has not received much attention in the context of data mining Now, as increasingly complex real-world problems are addressed, rarity, and the related problem of imbalanced data, are taking center stage This article discusses the role that rare classes and rare cases play in data mining The problems that can result from these two forms of rarity are described in detail, as are methods for addressing these problems These descriptions utilize examples from existing research So that this article provides a good survey of the literature on rarity in data mining This article also demonstrates that rare classes and rare cases are very similar phenomena---both forms of rarity are shown to cause similar problems during data mining and benefit from the same remediation methods

Support vector clustering

Abstract: We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.