Combining Meta-Learning and Optimization Algorithms for Parameter Selection

Supervised learning theory tells us that generalization in this case is poor (not enough target data and it is difficult to rely on data coming from different

Four distributions functions, the modified Lorentzian, Guassian, Gauss-Gauss and lognormal- Gauss are used and optimized to simulate the hysteresis loops. In the last step, the

Within corpus, we compare models trained in a speaker-specific setting, on a single speaker, with models trained in a speaker-independent setting, training on multiple speakers..

The application of the PSO method to adjust parameters of deep neural networks used for the machine learning process would be based on the fact that the PSO algo- rithm would

Kotthoff, “Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR),” in Proceedings of the 41st European Conference

Table 1: Illustrative example of the overall design of the augmented transaction table with amended learning subsystem performance scores, statistics and meta-learner information..

Although the performance of Additive Regression on its own is worse when compared directly with other two ensemble techniques such as Random Forest and Random Subspace in table

Global-level meta-learners use the characteristics of data to select the overall- best algorithm for entire datasets, or an entire platform (e.g. Mid-level meta-learners select