要在R语言中实现主动学习算法，您可以使用以下包和算法：
Package: activelearning
- This package provides several active learning algorithms, including uncertainty sampling, query by committee, and versionSpace.
Package: caret (Classification And REgression Training)
- This package provides interface functions for various machine learning algorithms, as well as tools for data splitting, pre-processing, feature selection, and model tuning. It also includes several functions for active learning such as sample selection and query construction.
Algorithm: Uncertainty sampling
- This algorithm selects the samples based on the uncertainty of the model predictions. It selects the samples with the highest degree of uncertainty, which will help reduce the model error.
Algorithm: Query by committee
- This algorithm selects the samples by voting among a set of models. It creates a committee of models from the labeled data and then selects the samples that have the highest disagreement among the committee members.
As for resources, you may refer to the following:
Active Learning: Theory and Applications – Book by Mykola Pechenizkiy and Jun Hu
Active Learning – Tutorial by Sargur N. Srihari from University of Buffalo
Active Learning – Tutorial by Andrew Ng from Stanford University
Active Learning – Article by Burr Settles