A kind of machine active learning method and learning system

Abstract

The invention discloses a machine active learning method and a learning system, which obtains classified corpus data by clustering original corpus data; automatically recommends the classified corpus data according to preset rules to obtain recommended corpus data; Recommend corpus data for manual labeling to obtain labeled corpus data; input the labeled corpus data into the test model for machine learning, and output learning results; thus organically combining supervised learning and unsupervised learning, while ensuring better learning effects Basically, the workload of manual labeling is greatly reduced, and the learning efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a machine active learning method and a system for applying the method. Background technique [0002] Machine learning is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines, specializing in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or Skills, reorganize the existing knowledge structure to continuously improve its own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent and automatic. Its application pervades all fields of artificial intelligence. [0003] At present, there are two main methods of machine learning, supervised learning and unsupervised learning. Unsupervised learning does not interfere with its learning content ...

AI Technical Summary

Problems solved by technology

Unsupervised learning does not interfere with its learning content at all, allowing machines to learn data independently and freely, saving a lot of labor costs, but the final learning effect is not good; supervised learning is artificially screening and preparing data for machines to learn, although the learning effect Better, but it takes a lot of manpower to prepare data for machine learning in the early stage

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