The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Rank-One Projections With Adaptive Margins for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
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Ore grade estimation is one of the key stages and the most complicated aspects in mining. Its complexity originates from scientific uncertainty. In this paper, a novel hybrid SLPSO-SVR model that hybridized the self-adaptive learning based particle swarm optimization (SLPSO) and support vector regression (SVR) is proposed for ore grade estimation. This hybrid SLPSO-SVR model searches for SVR's optimal parameters using self-adaptive learning based particle swarm optimization algorithms, and then adopts the optimal parameters to construct the SVR models. The SVR uses the 'Max-Margin' idea to search for an optimum hyperplane, and adopts the @e-insensitive loss function for minimizing the training error between the training data and identified function. The hybrid SLPSO-SVR grade estimation method has been tested on a number of real ore deposits. The result shows that method has advantages of rapid training, generality and accuracy grade estimation approach. It can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation.