Extremal Optimization Combined with LM Gradient Search for MLP Network Learning
نویسندگان
چکیده
منابع مشابه
Meta-learning via Search Combined with Parameter Optimization
Framework for Similarity-Based Methods (SBMs) allows to create many algorithms that differ in important aspects. Although no single learning algorithm may outperform other algorithms on all data an almost optimal algorithm may be found within the SBM framework. To avoid tedious experimentation a meta-learning search procedure in the space of all possible algorithms is used to build new algorith...
متن کاملCsMTL MLP For WEKA: Neural Network Learning with Inductive Transfer
We present context-sensitive Multiple Task Learning, or csMTL as a method of inductive transfer embedded in the well known WEKA machine learning suite. csMTL uses a single output neural network and additional contextual inputs for learning multiple tasks. Inductive transfer occurs from secondary tasks to the model for the primary task so as to improve its predictive performance. The WEKA multi-...
متن کاملNovel Mlp Neural Network with Hybrid Tabu Search Algorithm
In this paper, we propose a new global and fast Multilayer Perceptron Neural Network (MLP-NN) which can be used to forecast the automotive price. Nowadays, the gradient-based techniques, such as back propagation, are widely used for training neural networks. These techniques have local convergence results and, therefore, can perform poorly even on simple problems when forecasting is out of samp...
متن کاملA Learning Optimization Algorithm in Graph Theory - Versatile Search for Extremal Graphs Using a Learning Algorithm
Using a heuristic optimization module based upon Variable Neighborhood Search (VNS), the system AutoGraphiX’s main feature is to find extremal or near extremal graphs, i.e., graphs that minimize or maximize an invariant. From the so obtained graphs, conjectures are found either automatically or interactively. Most of the features of the system relies on the optimization that must be efficient b...
متن کاملExtremal Optimization: an Evolutionary Local-Search Algorithm
A recently introduced general-purpose heuristic for finding high-quality solutions for many hard optimization problems is reviewed. The method is inspired by recent progress in understanding far-from-equilibrium phenomena in terms of self-organized criticality, a concept introduced to describe emergent complexity in physical systems. This method, called extremal optimization, successively repla...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2010
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2010.9727728