{"paper":{"title":"Bounds on learning in polynomial time","license":"","headline":"","cross_cats":[],"primary_cat":"cond-mat.dis-nn","authors_text":"Anthea Bethge, Heinz Horner","submitted_at":"1997-05-26T14:06:00Z","abstract_excerpt":"The performance of large neural networks can be judged not only by their storage capacity but also by the time required for learning. A polynomial learning algorithm with learning time $\\sim N^2$ in a network with $N$ units might be practical whereas a learning time $\\sim e^N$ would allow rather small networks only. The question of absolute storage capacity $\\alpha_c$ and capacity for polynomial learning rules $\\alpha_p$ is discussed for several feed-forward architectures, the perceptron, the binary perceptron, the committee machine and a perceptron with fixed weights in the first layer and ad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"cond-mat/9705259","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}