增量学习
增量学习(或在线学习)需要处理来自数据流的传入数据,且可能对预测变量的分布、目标函数的各个方面,以及观测值是否带标签等知识都知之甚少或完全不了解。增量学习问题与传统的机器学习方法形成对比,在传统的机器学习方法中,有足够的带标签数据可用于模型拟合、执行交叉验证以调整超参数并推断预测变量分布特性。
增量学习需要经过配置的增量模型。您可以通过调用一个对象(例如 incrementalClassificationLinear
)直接创建和配置增量模型,也可以使用 incrementalLearner
将受支持的以传统方式训练的模型转换为增量学习器。在配置模型并设置数据流后,您可以将增量模型与传入的数据块进行拟合,跟踪该模型的预测性能,或同时执行这两项操作。
有关详细信息,请参阅Incremental Learning Overview。
您还能够以增量方式监控概念数据中的漂移,例如分类误差。首先,您需要使用 incrementalConceptDriftDetector
配置漂移检测器。在建立数据流后,您可以更新漂移检测器并使用 detectdrift
检查任何漂移。有关详细信息,请参阅参考页。
函数
对象
主题
- Incremental Learning Overview
Discover fundamental concepts about incremental learning, including incremental learning objects, functions, and workflows.
- Configure Incremental Learning Model
Prepare an incremental learning model for incremental performance evaluation and training on a data stream.
- Implement Incremental Learning for Classification Using Succinct Workflow
Use the succinct workflow to implement incremental learning for binary classification with prequential evaluation.
- Implement Incremental Learning for Classification Using Flexible Workflow
Use a flexible workflow to implement incremental learning for binary classification with prequential evaluation.
- Initialize Incremental Learning Model from Logistic Regression Model Trained in Classification Learner
Train a logistic regression model using the Classification Learner app, and then initialize an incremental model for binary classification using the estimated coefficients.
- Perform Conditional Training During Incremental Learning
Use the flexible workflow to implement conditional training during incremental learning with a naive Bayes multiclass classification model.
- Perform Text Classification Incrementally
This example shows how to incrementally train a model to classify documents based on word frequencies in the documents; a bag-of-words model.
- Incremental Learning with Naive Bayes and Heterogeneous Data
This example shows how to prepare heterogeneous predictor data, containing real-valued and categorical measurements, for incremental learning using a naive Bayes classifier.
- Monitor Equipment State of Health Using Drift-Aware Learning
This example shows how to automate the process of monitoring the state of health for a cooling system using an incremental drift-aware learning model and Streaming Data Framework for MATLAB® Production Server™.