Kernel Ridge Regression learns Hopfield network weights in closed form, matching the recall of Kernel Logistic Regression up to a storage load of 1.5 while training up to 20 times faster.
Kernel Logistic Regression Learning for High-Capacity Hopfield Networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Hebbian learning limits Hopfield network storage capacity (pattern-to-neuron ratio around 0.14). We propose Kernel Logistic Regression (KLR) learning. Unlike linear methods, KLR uses kernels to implicitly map patterns to high-dimensional feature space, enhancing separability. By learning dual variables, KLR dramatically improves storage capacity, achieving perfect recall even when pattern numbers exceed neuron numbers (up to ratio 1.5 shown), and enhances noise robustness. KLR demonstrably outperforms Hebbian and linear logistic regression approaches.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Kernel Ridge Regression for Efficient Learning of High-Capacity Hopfield Networks
Kernel Ridge Regression learns Hopfield network weights in closed form, matching the recall of Kernel Logistic Regression up to a storage load of 1.5 while training up to 20 times faster.