AT&T (United States)

The Nature of Statistical Learning Theory

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensional...

The Nature of Statistical Learning Theory

Gene Selection for Cancer Classification using Support Vector Machines

Novel methods improve prediction of species’ distributions from occurrence data

Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occ...

The art of modelling range-shifting species

1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequentl...

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial bas...

Single Molecules Observed by Near-Field Scanning Optical Microscopy

Individual carbocyanine dye molecules in a sub-monolayer spread have been imaged with near-field scanning optical microscopy. Molecules can be repeatedly detected and spatially ...

Generalized norm-conserving pseudopotentials

A method is introduced for computing norm-conserving pseudopotentials from all-electron atom potentials at arbitrary energies, rather than at bound-state energies only, as in ex...