Sparse coding is an important approach for the unsupervised learning of sensory features. In this contribution, we present two new methods that extend the traditional sparse coding approach with supervised components. Our goal is to increase the suitability of the learned features for classification tasks while keeping most of their general representation capability. We analyze the effect of th...

in order to assess the satellite data for soil investigation, aster digital data 20 june 2006, field study and phisiochemical properties of soil, were analyzed. all landcover classes including soils are classified based onmorphological and physico-chemical characteristics. images were geocorrected and photomorphic units were selected based upon visual interpretation and sampling in study area. ...

Abstract In this case study, we present opportunities science, technology, engineering, arts, and mathematics ( STEAM ) education provided to a sixth-grade class. We collected observational interview data in language arts science class over 1 year. used the liberation social psychology LSP framework understand students’ discourses discussions as they drew from engineering activities, ideas. Fur...

Customer classification and prioritization are commonly used in applications to provide queue preferential service. Their influence on queuing systems has been thoroughly studied from the delay distribution perspective. However, the fairness aspects, which are inherent to any preferential system and highly important to customers, have hardly been studied and not been quantified to date. We use ...

-A general pattern recognition problem is posed in Hilbert space. Two new solutions are then given and it is shown that the sensitivity of the pattern recognition functions to pattern perturbation can be a priori controlled. A series of examples demonstrate the principal results in a variety of settings. Pattern recognition Multilinearity Hilbert space Functional sensitivity I N T R O D U C T I...