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Employing different loss functions for the classification of images via supervised learning

100%

Boţ R., Heinrich A., Wanka G.

Open Mathematics

2014

tom 12

nr 2

381-394

Supervised learning methods are powerful techniques to learn a function from a given set of labeled data, the so-called training data. In this paper the support vector machines approach is applied to an image classification task. Starting with the corresponding Tikhonov regularization problem, reformulated as a convex optimization problem, we introduce a conjugate dual problem to it and prove that, whenever strong duality holds, the function to be learned can be expressed via the dual optimal solutions. Corresponding dual problems are then derived for different loss functions. The theoretical results are applied by numerically solving a classification task using high dimensional real-world data in order to obtain optimal classifiers. The results demonstrate the excellent performance of support vector classification for this particular problem.

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

88%

Calvet L., Armas J. d., Masip D., Juan A. A.

Open Mathematics

2017

tom 15

nr 1

261-280

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

Ograniczanie wyników

2 Open Mathematics

1 Armas J. d.

1 Boţ R.

1 Calvet L.

1 Heinrich A.

1 Juan A. A.

1 Masip D.

1 Wanka G.

1 2017

1 2014

Wyniki wyszukiwania

Employing different loss functions for the classification of images via supervised learning

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs