My past work has investigated methods for improving generalization of machine learning models via intrinsic motivation, transfer learning, multi-task learning, and learning from human preferences.
This change improves generalization of the integrated tone model, for example, timbre modeling and extension to a bayesian estimation.
We have argued that important information is transferred in the soft targets which improves generalization of the student network, resulting in the lower WERs and EERs observed in Sections 3, 4, and 5.
To improve generalization capability of SVM, the preprocessing of training data may include data smoothing, excluding odd values, and normalizing experimental data.
In order to determine the proper size of the reservoir and improve generalization ability of SCRN, a Sensitive Iterated Pruning Algorithm (SIPA), in which a larger than necessary reservoir is employed firstly and then its size is reduced by pruning out the least sensitive internal units, is proposed to optimize the reservoir size and weights of SCRN.
In this section, we present soft target training experiments to improve generalization ability of student DNNs on a LVCSR task.
To overcome this issue and to improve generalization ability of a neural network, a hybrid neural model with cross validation method is proposed.
Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.
Genetic algorithms are a powerful technique to select features for multi-task learning automatically; GA-MTL and e-GA-MTL are shown to to improve generalization performance of classifiers on microarray data sets.
Furthermore, our results demonstrate that genetic algorithms can be employed to improve multi-task learning by discarding irrelevant features and by selecting the input and target features automatically; GA-MTL and e-GA-MTL are shown to to improve generalization performance of classifiers on microarray and other high-dimensional data sets.
