The term ―feature‖ in Remote Sensing Image (RSI) takes its specific definition from the objective and scope of the study by the analyst. In spatial data mining using remote sensing satellite data, features mainly indicate objects constituting natural resources such as land, water and sea. This may broadly encompass vegetation, land condition, water quality, extent and types of vegetation and co...

Development of SAS ® linear models procedures over the past several years has led to a number of easily accessible methodological statistical advances for experimental data analysis. The original linear models program, GLM, was a fixed model procedure for analysis of normally distributed data with homogeneous variances. The GENMOD procedure extended the fixed linear model analysis to a number o...

In this paper we show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative class-specific features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear se...

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit m...

In areas of oil and gas exploration, seismic lines have been reported to alter the movement patterns of wolves (Canis lupus). We developed a mechanistic first passage time model, based on an anisotropic elliptic partial differential equation, and used this to explore how wolf movement responses to seismic lines influence the encounter rate of the wolves with their prey. The model was parametriz...

This paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. The key idea is to precompute as partial results the weights of primitive feature vectors that appear frequently in the target NLP task. A trie compactly stores the primitive feature vectors with their weights, and it enables the classifier to find for a given f...

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animal vocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equ...

This paper investigates the characterization ability of linear and nonlinear features and proposes combining such features in order to improve the classification of biological signals, in particular single-trial electroencephalogram (EEG) and electrocardiogram (ECG) data. For this purpose, three data sets composed of ECG, epileptic EEG and finger-movement EEG were utilized. The characterization...

Andriani Skopeliti 1 Lysandros Tsoulos 2 Cartography Laboratory, Faculty of Rural and Surveying Engineering National Technical University of Athens H. Polytechniou 9, 157 80 Zographou Campus, Athens, Greece Tel: +30 +1+772-2730 Fax: +30+1+772-2734 email: [email protected], [email protected] ABSTRACT Generalization of linear features is considered to be among the most important genera...