摘要翻译：
计算生物学中的一个主要问题是现有的分类模型无法融入不断扩展的新领域知识。本文通过引入生物信息学中的增量学习来解决静态分类模型的问题。许多机器学习工具已经被应用于这个问题，它们使用静态的机器学习结构，如神经网络或支持向量机，这些结构无法将新的信息容纳到它们现有的模型中。我们利用模糊ARTMAP作为一个替代的机器学习系统，它具有增量学习新数据的能力。模糊ARTMAP被发现可以与许多广泛的机器学习系统相媲美。在选择和组合单个分类器的集成系统中使用进化策略，加上模糊ARTMAP的增量学习能力，证明了它适合作为模式分类器。利用G-偶联蛋白受体数据库的数据对该算法进行了测试，准确率为83%。所提出的系统也是普遍适用的，可用于基因组学和蛋白质组学中的问题。
---
英文标题：
《An Adaptive Strategy for the Classification of G-Protein Coupled
  Receptors》
---
作者：
S. Mohamed, D. Rubin, and T. Marwala
---
最新提交年份：
2007
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
--

---
英文摘要：
  One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning structures such as neural networks or support vector machines that are unable to accommodate new information into their existing models. We utilize the fuzzy ARTMAP as an alternate machine learning system that has the ability of incrementally learning new data as it becomes available. The fuzzy ARTMAP is found to be comparable to many of the widespread machine learning systems. The use of an evolutionary strategy in the selection and combination of individual classifiers into an ensemble system, coupled with the incremental learning ability of the fuzzy ARTMAP is proven to be suitable as a pattern classifier. The algorithm presented is tested using data from the G-Coupled Protein Receptors Database and shows good accuracy of 83%. The system presented is also generally applicable, and can be used in problems in genomics and proteomics.
---
PDF下载：
-->