Research on feature coding theory and typical application analysis in machine learning algorithms

Abstract

Nowadays, the world is still in the environment of economic depression. In order to promote economic recovery, improve Relations of production and production efficiency, stimulate consumption expansion and upgrading, and accelerate industrial transformation and upgrading, problems such as industrial upgrading need to be solved urgently. Solving the above problems requires more useful tools, and artificial intelligence is one of them. Machine learning is the key to distinguishing artificial intelligence from ordinary program code. Unlike people learning knowledge, machine learning has its own unique language algorithms and behavioral logic. Machine learning, as a technology active in the field of artificial intelligence in recent years, specializes in studying how computers learn, simulate and realize part of human learning behavior, so as to provide data mining and behavior prediction for humans, to obtain new knowledge or skills, or to strengthen the original basic ability of machines. In this study, a variety of common coding algorithms and learning strategies in machine learning are discussed, supervised learning algorithms are selected as examples in the learning strategies, models are further selected and evaluated for a variety of algorithms, and parameters are adjusted and performance is analyzed. As for the theoretical analysis in the research, the paper makes a tentative application in the three fields of housing price, physical store sales and digital recognition, explores and selects the corresponding application method in the appropriate scenario, and expands the application field of machine learning.