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The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization
[article]
2018
arXiv
pre-print
We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The method is based on the invariances or stability of predicted results when known data is represented as expansions in terms of various stochastic functions. The algorithm predicts the classification/regression values of new data by combining (via voting) the outputs of these numerous linear expansions in randomly chosen functions. The few parameters (typically only
arXiv:1708.05715v3
fatcat:pf2lkcapbbbe5mxuzlsq2ovrwy