仅用一张样本即可精准分类高度相似文档，像人一样学习概念。
Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents
- 通过捕捉文档结构特征实现人类级概念学习
- 单样本分类准确率超越传统模型和深度学习方法
- 适合资源受限场景，解释性强且环保节能
人类通常只需一个例子就能掌握新概念，而机器学习模型往往需要数百个样本。本文提出坐标矩阵机（CM²），一种专为小规模设计的绿色AI模型，通过识别文档中人类会关注的结构化关键特征，实现对高度相似文档的分类。该模型不依赖大规模预训练和高性能硬件，仅需每类一个样本即可完成学习。实验表明，其性能优于传统向量化方法及复杂的深度学习模型，具备高精度、几何与结构感知能力、可解释性（透明模型）、低延迟、抗类别不平衡、计算成本低等优势，适用于仅靠CPU运行的环境，兼具可持续性与经济可行性。
原文摘要 · Abstract (English)
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 present the Coordinate Matrix Machine (CM$^2$). This purpose-built small model augments human intelligence by learning document structures and using this information to classify documents. 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 vectors, CM$^2$ offers: 1. High accuracy with minimal data (one-shot learning) 2. Geometric and structural intelligence 3. Green AI and environmental sustainability 4. Optimized for CPU-only environments 5. Inherent explainability (glass-box model) 6. Faster computation and low latency 7. Robustness against unbalanced classes 8. Economic viability 9. Generic, expandable, and extendable
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