By combining lower level features to form more abstract, higher level representing property classifications or features, deep learning is to discover distributed feature representation of data [2].
The results indicate that with a combination of two morphological and four texture properties, classification levels attain 91 94% accuracy, depending on the type of classification model (LDA or KNN).
Four pedons from the alluvial plain of Drama (NE Greece) were investigated in order to provide detailed information on their morphology, chemical, physical and mineralogical properties, classification and genesis.
This is a good property for classification.
In this sense, this kind of linear supervised distance in SLLE is not a good property for classification since it will go to a great extent to decrease the discriminating power of the low-dimensional embedded data representations produced with SLLE.
This property enables classification trees, unlike logistic regression models, to be readily interpreted by people with little statistical knowledge.
A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.
Chemical composition and internal structure together constitute the essence of a mineral and determine its physical properties; thus, classification should rely on both.
To help reconstruct paleoenvironments and for ease of comparison, we suggest a property-based classification system linked to genetic processes.
In addition, property based classifications such as vitamins, flavours and fragrances, drugs and FEMA compounds have been defined through lists of specific compounds.
