Support Vector Machine
- Introduction
- Installing/Configuring
- Examples
- SVM — The SVM class
- SVM::__construct — Construct a new SVM object
- SVM::crossvalidate — Test training params on subsets of the training data.
- SVM::getOptions — Return the current training parameters
- SVM::setOptions — Set training parameters
- SVM::train — Create a SVMModel based on training data
- SVMModel — The SVMModel class
- SVMModel::checkProbabilityModel — Returns true if the model has probability information
- SVMModel::__construct — Construct a new SVMModel
- SVMModel::getLabels — Get the labels the model was trained on
- SVMModel::getNrClass — Returns the number of classes the model was trained with
- SVMModel::getSvmType — Get the SVM type the model was trained with
- SVMModel::getSvrProbability — Get the sigma value for regression types
- SVMModel::load — Load a saved SVM Model
- SVMModel::predict_probability — Return class probabilities for previous unseen data
- SVMModel::predict — Predict a value for previously unseen data
- SVMModel::save — Save a model to a file
- Constant hash database
- Клиентская библиотека работы с URL
- Event
- File Alteration Monitor
- FTP
- Gearman
- Net Gopher
- Gupnp
- Hyperwave API
- Облегчённый протокол доступа к каталогам (LDAP)
- Memcache
- Memcached
- mqseries
- Network
- RRDtool
- Simple Asynchronous Messaging
- SNMP
- Сокеты
- Secure Shell2
- Stomp Client
- Support Vector Machine
- Subversion
- TCP Wrappers
- Varnish
- YAZ
- YP/NIS
- ZMQ
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