Sentence examples for based classification can be from inspiring English sources
Measures like classification accuracy and architectural complexity used for evaluating PNN based classification can be thought of as two different conflicting criterions.
Also, the likelihood based classification can be tuned by setting the decision threshold.
The basic performance resemblance of EW + LSSC discretization to L1 distance-based classification can be extended to Lp distance-based and inner product-based classifications either by flexibly modifying the matching function or by substituting every continuous feature element individually with its binary approximate to obtain a similar classification behaviour in the continuous domain.
However, machine learning-based classification can be used for image segmentation and high-level analysis of image-derived features [ 169].
In summary, we show that the SVM-based classification can be successfully applied for discriminating between MSMG and non-MSMG, whereas combining heterogeneous data types improves learning.
The limitation of the KEGG based classification can also been seen in the poor representation of Tortula clusters in the other KEGG pathways (Genetic Information Processing, Environmental Information processing, and Cellular Processing) which is surprising given the number of clusters that were identified in the BLASTx search and in the GO databases as being contained within these classifications.
The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset.
Combining information from text, images, videos, molecular data or structured data in knowledge bases to improve classification can be facilitated through the use ontologies, by first extracting relevant features from each type of information and representing the results using a single ontology that combines the information used for training a classifier.
An elementary classification can be based on application to 2D or 3D shapes (see Fig. 1).
