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Found 5 papers, 1 papers with code
Domain Knowledge integrated for Blast Furnace Classifier Design
Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace system.
Transfer Learning in Information Criteria-based Feature Selection
Our theoretical results indicate that, for any sample size in the target domain, the proposed TLCp estimator performs better than the Cp estimator by the mean squared error (MSE) metric in the case of orthogonal predictors, provided that i) the dissimilarity between the tasks from source domain and target domain is small, and ii) the procedure parameters (complexity penalties) are tuned according to certain explicit rules.
Knowledge Integrated Classifier Design Based on Utility Optimization
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge.
Efficacy of regularized multi-task learning based on SVM models
This paper investigates the efficacy of a regularized multi-task learning (MTL) framework based on SVM (M-SVM) to answer whether MTL always provides reliable results and how MTL outperforms independent learning.
Enhancing Interpretability of Black-box Soft-margin SVM by Integrating Data-based Priors
The lack of interpretability often makes black-box models difficult to be applied to many practical domains.
