Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents

February 09, 2026

Reading time: 1 minute

...

📝 Original Info

Title: Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents
ArXiv ID: 2512.23749
Date: 2025-12-26
Authors: Amin Sadri, M Maruf Hossain

📝 Abstract

Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. Contribution: In this paper, we introduce Coordinate Matrix Machine (CM 2 ), a structureaware document intelligence model designed to bridge the gap between semantic content and spatial layout. Unlike traditional NLP approaches that prioritize sequential text, CM 2 is particularly effective for low-data, layout-sensitive tasks, enabling high-precision performance when structural geometry is paramount. While modern "Red AI" trends rely on massive pre-training and energy-intensive GPU infrastructure, CM 2 is designed as a Green AI solution. It achieves human-level concept learning by identifying only the structural "important features" a human would consider, allowing it to classify very similar documents using only one sample per class. Advantage: Our algorithm outperforms traditional vectorizers and complex deep learning models that require larger datasets and significant compute. By focusing on structural coordinates rather than exhaustive semantic v...

📄 Full Content

...(본문 내용이 길어 생략되었습니다. 사이트에서 전문을 확인해 주세요.)

Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents

📝 Original Info

📝 Abstract

📄 Full Content

Table of Contents

Table of Contents

📝 Original Info

📝 Abstract

📄 Full Content

Start searching

No results found