Apr 10, 2024 · Big Data

Bloom Filter: Principles, False Positive Rate, and Implementations with Guava and Redis

Bloom filters are space‑efficient probabilistic structures that answer “definitely not” or “maybe” membership queries, with a controllable false‑positive rate derived from bit array size, element count, and hash functions, and can be implemented via Guava’s Java library, Redisson’s Redis wrapper, native Redis modules, or custom bitmap code, dramatically reducing memory usage and latency in large‑scale systems such as URL deduplication or user‑product checks.

Big DataGuavaJava

0 likes · 21 min read

Bloom Filter: Principles, False Positive Rate, and Implementations with Guava and Redis

Alibaba Cloud Developer

Aug 15, 2018 · Artificial Intelligence

How τ‑FPL Reduces False Positives in High‑Risk Classification Tasks

τ‑FPL introduces a novel ranking‑threshold approach that explicitly incorporates a false‑positive‑rate constraint into binary classifier training, offering linear‑time optimal solutions, theoretical error bounds, and superior experimental performance on high‑risk tasks such as disease monitoring and autonomous driving.

Neyman-Pearsonfalse-positive-ratelinear-time

0 likes · 11 min read

How τ‑FPL Reduces False Positives in High‑Risk Classification Tasks