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SpamBayes is a Bayesian spam filter written in Python which uses techniques laid out by Paul Graham in his essay "A Plan for Spam". It has subsequently been improved by Gary Robinson and Tim Peters, among others.
Web filtering & Overview
Explore the main themes, entities and connections around SpamBayes. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| SpamBayes | Available in | English only | 1.00 | infobox |
| SpamBayes | License | PSFL | 1.00 | infobox |
| SpamBayes | Original author | Tim Peters | 1.00 | infobox |
| SpamBayes | Platform | Cross-platform | 1.00 | infobox |
| SpamBayes | Preview release | 1.1a6 / December 6, 2008 (2008-12-06) | 1.00 | infobox |
| SpamBayes | Release | September 2002 | 1.00 | infobox |
| SpamBayes | Stable release | 1.0.4 / March 2005 | 1.00 | infobox |
| SpamBayes | Type | E-mail filtering | 1.00 | infobox |
| SpamBayes | Website | spambayes.sourceforge.net | 1.00 | infobox |
| SpamBayes | Written in | Python | 1.00 | infobox |
| SpamBayes | is a | Bayesian spam filter written in Python which uses techniques laid out by Paul Graham in his essay | 0.90 | text |
| SpamBayes | related to External links | Official | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.