MCS
2005
Springer
14 years 5 months ago
2005
Springer
MCS
2005
Springer
14 years 5 months ago
2005
Springer
Abstract. Obtaining ground truth for hyperspectral data is an expensive task. In addition, a number of factors cause the spectral signatures of the same class to vary with location...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribut...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
Mixed Group Ranks is a parametric method for combining rank based classi ers that is eective for many-class problems. Its parametric structure combines qualities of voting methods...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
A Half-Against-Half (HAH) multi-class SVM is proposed in this paper. Unlike the commonly used One-Against-All (OVA) and One-Against-One (OVO) implementation methods, HAH is built ...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
Abstract. Multiclass SVMs are usually implemented by combining several two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method imple...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
This paper describes a unified approach, based on Gaussian Processes, for achieving sensor fusion under the problematic conditions of missing channels and noisy labels. Under the ...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
We study how the error of an ensemble regression estimator can be decomposed into two components: one accounting for the individual errors and the other accounting for the correlat...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
After deploying a classifier in production it is essential to support its lifecycle. This paper describes the application of an ensemble of classifiers to support two stages of the...
MCS
2005
Springer
14 years 5 months ago
2005
Springer
We describe an ensemble learning approach that accurately learns from data that has been partitioned according to the arbitrary spatial requirements of a large-scale simulation whe...