# Install tinysvm with Homebrew, MacPorts Support vector machine library for pattern recognition. Version 0.09 via Homebrew; verified 2026-06-19. ## Install ```sh sudo av install brew:tinysvm ``` Additional install commands: ### macOS - Homebrew (100%): ```sh brew install tinysvm ``` Evidence: local Homebrew formula metadata - MacPorts (94%): ```sh sudo port install TinySVM ``` Evidence: MacPorts ports tree: math/TinySVM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ## Package facts - **Package key:** brew:tinysvm - **Package manager:** Homebrew - **Package manager page:** - **Version:** 0.09 - **Source summary:** Support vector machine library for pattern recognition - **Homepage:** - **Upstream docs:** - **License:** LGPL-2.1-or-later - **Source archive:** - **Last updated:** 2026-06-19T12:33:03-07:00 - **Generated:** 2026-07-08T07:18:31+00:00 ## Executables - svm_classify (cli) - svm_learn (cli) - svm_model (cli) - svm_classify (alias) - svm_learn (alias) - svm_model (alias) ## Install behavior - Post-install hook: not defined - Bottle: available on arm64_big_sur, arm64_linux, arm64_monterey, arm64_sequoia, arm64_sonoma, arm64_tahoe, arm64_ventura, big_sur, catalina, monterey, sonoma, ventura ## Freshness - Page generated: 2026-07-08 - Package-manager version: 0.09 - Package-manager updated: 2026-06-19 - Local data: ok - Upstream repository: http://chasen.org/~taku/software/TinySVM/ - info: Release/tag comparison is only available for GitHub repositories. ## Project history and usage TinySVM is an early-2000s C++ support vector machine package by Taku Kudo for pattern-recognition work. It shipped both library APIs and small command-line tools, which is why it survives as a niche package-manager artifact long after the mainstream machine-learning world moved toward larger Python-centered stacks. ### Project history The official TinySVM page describes it as an implementation of Support Vector Machines for pattern recognition, citing Vapnik's SVM work and positioning SVMs as then-new statistical learning algorithms for practical tasks such as text categorization and handwritten character recognition. Its own examples identify the package as 'TinySVM - tiny SVM package' and show a 2000 copyright line in the learner output. The release notes show active development from at least January 2001 through August 2002. During that period TinySVM added support vector regression, Ruby bindings, RBF/Neural/ANOVA kernels, SWIG-based Perl and Ruby bindings, Python and Java interfaces, incremental training support, one-class SVM support, Mac OS X support, and Windows compiler support. TinySVM was distributed in a very package-nerd friendly way for its era: source tarballs, Red Hat 6.x and 7.x RPM/SRPM directories, Windows binaries, and anonymous CVS checkout instructions from the author's site. The official page says development used CVS and invited users to join CVS-based development. ### Adoption history TinySVM's adoption appears to have been strongest among early SVM users who wanted a small Unix/Windows package with command-line tools and language bindings. The official feature list emphasizes sparse vectors, tens of thousands of training examples, hundreds of thousands of feature dimensions, LRU cache storage for Gram matrices, and optimizations inspired by SVM_light. In modern package-manager culture it is mostly a preserved scientific-computing tool. The input metadata lists Homebrew and MacPorts packages, which suggests its current visibility is strongest among users maintaining old pipelines, comparing classic SVM implementations, or needing the exact svm_learn/svm_classify/svm_model command set. ### How it is used The command-line workflow is train, classify, and inspect: svm_learn reads training data and writes a model, svm_classify evaluates or interactively classifies test examples using that model, and svm_model displays model properties such as margin, VC dimension, and support-vector counts. TinySVM accepts the same sparse training-data representation as SVM_light, using class labels followed by feature:value pairs. The official docs call out this format because it can represent large sparse feature vectors, an important fit for text and pattern-recognition workloads of the time. ### Why package nerds care TinySVM matters to package nerds as a compact fossil from the pre-scikit-learn era: a tarball/CVS-era ML library with CLI programs, RPMs, Windows binaries, and multiple scripting-language bindings. It is small enough to package, old enough to need compatibility care, and recognizable by its SVM_light-style data format. ### Timeline - 2000: Official command examples identify the package as TinySVM and show a 2000 copyright line. - 2001-01-17: Version 0.02 added support vector regression and a Ruby module. - 2001-09-03: RBF, Neural, and ANOVA kernels were added; SWIG-based bindings and Python/Java interfaces became available. - 2001-12-07: Experimental one-class SVM support was added. - 2002-03-08: Mac OS X support was added. - 2002-08-20: TinySVM 0.09 was released with compiler and Windows build updates. ### Related projects - SVM_light is the closest implementation reference: TinySVM documents compatible sparse data representation and optimization algorithms stemming from SVM_light. - SWIG is relevant because TinySVM used it to provide scripting-language bindings. - LIBSVM is a related classic SVM package from the same general era, though not cited on the official TinySVM page. ### Sources - - source_facts.package-manager ## Security Notes library-like package without higher-risk signals. - **Geiger risk:** green / low - library-like package without higher-risk signals ## Source Database Details - **Source Database:** Homebrew formula API - **Tap:** homebrew/core - **Full Name:** tinysvm - **Version Scheme:** 0 - **Revision:** 0 - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** stable ## Other Package-Manager Records - MacPorts - TinySVM: normalized package name match | MacPorts ports tree: math/TinySVM/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1 ## Related links - [Terminal utility packages](https://www.automicvault.com/pkg/terminal-utilities/) - Matched terminal and command-line workflow metadata. - [Networking and protocol packages](https://www.automicvault.com/pkg/networking-protocol-tools/) - Matched network, protocol, or remote-service metadata. - [Scientific computing packages](https://www.automicvault.com/pkg/scientific-computing-tools/) - Matched scientific computing metadata. - [Web development packages](https://www.automicvault.com/pkg/web-dev-tools/) - Matched web development metadata. - [yamcha](https://www.automicvault.com/pkg/brew/yamcha/) - Popular package that depends on this formula. - [flann](https://www.automicvault.com/pkg/brew/flann/) - Shares av.db curated category or tags: cli, library, machine-learning, science. - [liblinear](https://www.automicvault.com/pkg/brew/liblinear/) - Shares av.db curated category or tags: classification, cli, machine-learning, science. - [mlpack](https://www.automicvault.com/pkg/brew/mlpack/) - Shares av.db curated category or tags: cli, library, machine-learning, science. - [gibbslda](https://www.automicvault.com/pkg/brew/gibbslda/) - Shares av.db curated category or tags: cli, machine-learning, science. - [grt](https://www.automicvault.com/pkg/brew/grt/) - Shares av.db curated category or tags: cli, machine-learning, science. - [openvino](https://www.automicvault.com/pkg/brew/openvino/) - Shares av.db curated category or tags: cli, machine-learning, science. - [btllib](https://www.automicvault.com/pkg/brew/btllib/) - Shares av.db curated category or tags: cli, library, science. - [clhep](https://www.automicvault.com/pkg/brew/clhep/) - Shares av.db curated category or tags: cli, library, science. ## Combined YAML source View the package source record on GitHub. [combined/tinysvm.yml](https://github.com/automic-vault/db/blob/main/combined/tinysvm.yml) ## Sources - Nucleus package database - Geiger risk classifier - package-page enrichment - curated package history - package version freshness - av.db category and tag curation - package relationship graph - external package-manager database matches - cross-ecosystem install command graph