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arXiv Machine Learning By Radha Poovendran, Andrea Stocco, Linda Bushnell

AIBL: Augmented Instance-Based Learning with Structured Memory and Neural Embeddings

Read the original on arXiv Machine Learning →

AIBL (Augmented Instance-Based Learning) extends traditional Instance-Based Learning Theory by embedding high‑dimensional sequential data into a learned vector space, enabling neural similarity matching while preserving instance storage, activation‑weighted retrieval, and utility blending. The model introduces structured memory stores—active, forgotten, and surprise—to manage novelty detection and concept drift, promoting frequently surprising instances into active memory. Experiments on five machine‑learning tasks and three simulations show that AIBL outperforms classical IBLT variants and task‑specific baselines, improving accuracy by 6 to 17 percentage points.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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