Multiple Algorithms for Active Learning and Semi-Supervised Learning

This learning resource integrates multiple algorithms from both active learning and semi-supervised learning domains. These algorithms not only enhance learning efficiency but also make the learning process more engaging and challenging. The implementation includes key techniques such as query strategy functions for selecting the most informative samples in active learning, and label propagation methods for semi-supervised scenarios. The comprehensive application of these algorithms makes this resource complete and thorough, enabling students to gain a holistic understanding and mastery of the knowledge. Furthermore, through utilizing these algorithms with appropriate parameter tuning and optimization techniques, students can actively participate in the learning process and exercise their subjective initiative, thereby deepening their comprehension and retention of knowledge. This resource serves not only as valuable educational material but also as a catalyst for stimulating students' learning interest and unleashing their potential through hands-on coding exercises.