Jihun Yun, Peng Zheng, et al.
ICML 2019
We present results of computational experiments with an extension of the Perceptron algorithm by a special type of simulated annealing. The simulated annealing procedure employs a logarithmic cooling schedule c(k) = Γ/1n(k + 2), where Γ is a parameter that depends on the underlying configuration space. For sample sets S of n-dimensional vectors generated by randomly chosen polynomials w1 · x1a1 + ··· + wn · xnan ≥ θ, we try to approximate the positive and negative examples by linear threshold functions. The approximations are computed by both the classical Perceptron algorithm and our extension with logarithmic cooling schedules. For n = 256, ... , 1024 and ai = 3, ... , 7, the extension outperforms the classical Perceptron algorithm by about 15% when the sample size is sufficiently large. The parameter Γ was chosen according to estimations of the maximum escape depth from local minima of the associated energy landscape.
Jihun Yun, Peng Zheng, et al.
ICML 2019
Michael Muller, Anna Kantosalo, et al.
CHI 2024
Benjamin N. Grosof
AAAI-SS 1993
P.C. Yue, C.K. Wong
Journal of the ACM