In the simplest instance of binary classification, each -dimensional example has an associated yes/no attribute ; the SVM performs the classification by finding a hyperplane that divides the parameter space into two distinct regions, each of which ideally contains points from only one of the categories.
We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction.
We have demonstrated the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction.
we train a new instance of each potential classification model (SVM, HMMs, etc).. Figure 4 presents the results.
If all possible instances belonging to each case fall into one of the classe, and each class contains statistically representative instances, then the performance of classification is good enough.
In general, the same tumour types are recognised in humans and dogs; for instance, the classification of soft tissue sarcomas of dogs largely follows the human system [ 3].
Figure 2 demonstrates that there are already several instances of mis-classification present even on "perfect" data.
For instance, molecular classification of leukemia and lymphomas has tremendously enhanced our understanding of these diseases [2], [3], [4].
The changing parts of the molecules are described by a so called imaginary transition state (ITS) [ 1, 2] that allows, for instance, a classification of chemical reactions [ 3- 5].
The presence of missed events will likely diminish accuracy of classification (for instance, because individuals who actually experienced an event but were admitted elsewhere are mislabeled), although we cannot exclude the possibility that some apparent predictors of lower risk actually predict failure to follow-up within these health systems.
