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This is fact is well exemplified by the classification task undertaken in our study in which we found that the models optimized with linear kernel functions are less accurate as compared to those trained with non-linear functions.
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It revealed that for the great majority of active ligands, there were found inactive compounds from ZINC that were characterized by high similarity, therefore the classification task (discrimination between actives and inactives) was not trivial.
We start by illustrating the difficulty of the classification task at hand, in order to calibrate our expectations.
For that, we investigate the approach of representing malicious files by OpCode expressions as features in the classification task.
The classification task is performed by support vector machines to enhance the generalization ability of LeNet5.
The difficulty of the classification task is increased by an increasing number of classes, but including such decision rules could improve the rates obtainable.
In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets.
The most informative queries, given the goals of the classification task, are selected by the learning algorithm instead of being randomly selected as is the case in passive supervised learning.
The domain knowledge inherent in the classification task is captured by defining a suitable kernel function k x, x ′), which computes the similarity between two examples x and x ′.
Performance of HMAX and VisNet on the classification task was measured by the proportion of images classified correctly using a linear support vector machine (SVM) on all the C2 cells in HMAX [chosen as the way often used to test the performance of HMAX (Mutch and Lowe 2008; Serre et al. 2007a, b)] and on all the layer 4 (output layer) cells of VisNet.
The classification task is carried out by processing radar measurements only, no class (feature) measurements are used.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com