Sentence examples for more complex classifiers from inspiring English sources

For higher levels of distributional complexity, the linear classifier degrades, but so do the more complex classifiers owing to insufficient training data.

By combining more complex classifiers in a cascade structure, the speed of detector is increased and focused on the regions of the picture.

Second the distinction between normal and pathological voices is simply based on the correlation between acoustic features, while more complex classifiers are common in literature.

Note that a design of more complex classifiers (quadratic, for example), instead of the linear classifier proposed in this paper, would certainly improve the efficiency of the classification.

In the author identification experiments, we have only made use of the top-1000 usince since a larger set of potential classes would have been hard to cope with for the more complex classifiers that have been used.

The face detection is performed by means of the well-known Viola-Jones [87] approach (which combines increasingly more complex classifiers in a cascade) based detectors for face and head-shoulder (which helps to handle low resolution images) detection.

Hence, if one were to obtain a good result with a more complex classifier, it is most likely that the distributional complexity is low and there is no gain over using a linear classifier.

The second set is used to train the more complex classifier (operating in the uncertainty conditions).

It is possible that further fine tuning of parameters for these more-complex classifiers (in the sense of an implementable decision boundary) could have improved predictive performance.

The list of classifiers in Table 1 is representative for the most important families of classification methods, starting from simple classical methods such as linear discriminant analysis (LDA) and k-nearest neighbour (k-NN) up to more complex nonlinear classifiers such as random forests and neural networks.

Since this dataset is very challenging, we believe our method can achieve a better result by using a more complex nonlinear classifier learned from a supervised way.