The optimal classification problem can be transformed into a constrained optimization problem as follows: mathrm{minse}varphi left w,bright)=frac{1}{2}{leftVert wrightVert}^2 (7) mathrm{st}.kern0.5em {y}_ileft({x}_icdot w+bright -1ge 0kern0.75em i=1,2,cdots l (8).
Instead of assigning a subjective definition to each vote as liberal or conservative, it instead uses an automated process called optimal classification.
Only with optimal parameter can the optimal classification result be gained.
Support Vector Machine (SVM) evolves from the optimal classification of linearly separable cases.
The optimal classification of macroblocks and the optimal channel rate allocation are achieved by iterating two interdependent steps.
According to the figure, we can select an optimal pattern recognition algorithm to achieve the optimal classification effect.
This methodology is under the guidance of two optimal classification theories, a new classification theory for designing optimal classifiers with controlled error rates and the C.K. Chow's optimal classification theory for designing optimal classifiers with controlled conditional error.
They got a complete and optimal classification on non-simultaneous and simultaneous blow-ups by four sufficient and necessary conditions.
We show that the theoretically optimal Bayesian classification can also achieve optimal classification in practice and provides a realistic interpretation of the world where land cover classes intergrade gradually.
This formula represents an optimal classification hyperplane, which can minimize the classification error rate and maximize the classification interval in the meantime.
