Exploration of machine algorithms based on deep learning model and feature extraction.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Traditional lifelong machine learning faces challenges with insufficient labeling, high dimensionality, and inconsistent data distribution.
• Existing methods often struggle to efficiently update models without forgetting previous knowledge.

Purpose of the Study:

• To propose a novel deep learning model that integrates feature representation and advanced algorithms to overcome limitations in lifelong machine learning.
• To enhance the knowledge base and efficiency of lifelong learning systems through optimized feature extraction and model iteration.

Main Methods:

• A new deep learning model is proposed, combining feature representation with deep learning algorithms.
• The study analyzes and compares the performance of the optimized model against representative algorithms like ELLA and HLLA.
• A composite algorithm, Hierarchical Lifelong Learning Algorithm (HLLA), is developed using genetic algorithms and mutual information feature extraction.

Main Results:

• The optimized deep learning model based on the HLLA algorithm achieved a classification error rate of 0.63% at K = 1200.
• The integration of a feature model into the lifelong learning iteration process significantly improved the knowledge base.
• The model demonstrated excellent performance in unsupervised database algorithms.

Conclusions:

• The proposed deep learning model effectively addresses key challenges in lifelong machine learning, including data requirements and efficiency.
• The HLLA algorithm and feature integration offer a valuable approach to enhance model performance and reduce the need for extensive labeling.
• This research contributes to advancing lifelong machine learning by improving knowledge retention and learning efficiency.