This paper presents a simple and effective classification search system based on the classes information provided by search engines.
本文基于搜索引擎的分类信息，实现了一个简单有效的分类搜索系统。
Search and classification are often integrated together in a single system.
搜索和分类常常集成在一个系统中。
For example, a product list may be managed in an ERP system, but that list can be utilized by other systems (such as an ECM, Auto-classification, or Search system).
例如，产品列表可能在ERP系统中进行管理，但是其他系统(比如ecm、自动分类或搜索系统)可以利用这个列表。
This article takes the viewpoint of how OmniFind can be integrated with SchemaLogic Suite and ICM into a true enterprise search system leveraging the power of taxonomies and auto-classification.
本文主要关注omnifind如何与SchemaLogic Suite和ICM集成在一起，形成一个真正的企业搜索系统，可以利用分类法和自动分类的强大功能。
This system includes Document Management, Flow Management, Product Structure Management, Project Management, Classification and Search Management, and Dynamic Popedom Management.
本系统包括文档管理、流程管理、产品结构管理、项目管理、分类与检索管理和动态权限管理六个模块。
The system contains a Web interface for search, a module for classification of figures based on vector representations of figure legends and a module for indexing gene names.
该系统包含了用于搜索的一个网络界面，基于图片图例的向量表示的图片分类的一个模块，和用于索引基因名字的一个模块。
Automobile sales management system provides a convenient way to search, classification can be retrieved from the car, find their favorite car, fast and accurate.
汽车销售管理系统提供了方便的检索途径，可以从汽车分类进行检索，查找自己心仪的爱车，快捷准确。
Methods: After literature search and analysis, the authors introduced the decision body, classification and contents of international Grading Nursing system.
方法：通过文献检索和实证研究，从分级护理的决策主体、分类、内容等方面对国外分级护理进行介绍。
Our work inspired from previous work, exploiting fuzzy classification theory to improve the process of feature quantification, is implementing a small image search system.
我们的工作在前人的基础上利用模糊分类理论，改进了特征量化的过程，实现了一个小型的图片搜索系统。
So far, retrieval system is mainly based on the keyword search and classification technology.
目前已有的检索系统主要采用基于关键字的全文检索以及分类检索技术。
The classification results of basis classifiers were added to original feature vector to obtain the meta-level data, which enhanced the search ability in hypothesis space and decreased system bias.
通过扩展特征向量，元学习增强了对假设空间的表达能力，降低了系统的偏差。
The classification results of basis classifiers were added to original feature vector to obtain the meta-level data, which enhanced the search ability in hypothesis space and decreased system bias.
通过扩展特征向量，元学习增强了对假设空间的表达能力，降低了系统的偏差。
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