Research on improving accuracy and efficiency of animal data collection and classification using machine learning

Abstract

With the continuous expansion of machine learning algorithms in various application domains, the application value of new algorithms, such as Support Vector Machines and Convolutional Neural Networks in data classification, has garnered increasing attention. This paper takes machine learning algorithms as the research entry point, explores the concept of machine learning, and delves into its application value in data classification. This paper, starting with an overview of machine learning algorithms, analyzes the supervised and unsupervised learning problems in machine learning, focusing on the applications of Convolutional Neural Networks, Support Vector Machine models, and logistic regression algorithms in data classification. This study emphasizes designing and implementing a machine learning-based image classification system. Through an in-depth exploration of the application of machine learning algorithms in data classification, a fully functional system is constructed, encompassing multiple modules, including machine vision and software development. This system accurately classifies and recognizes images, providing practical tools and technical support for image processing and analysis. In this study, the goal of achieving good image classification is realized through research and the application of machine learning algorithms. By designing and implementing a machine learning-based image classification system, the accuracy and efficiency of classification in handling massive data are improved. This system also demonstrates wide-ranging prospects in software development and machine vision, among other fields.