搜索资源列表
Gradient_Feature_Selection_for
- 关于on-line boosting的gradient feature selection的介绍,With regard to on-line boosting the introduction of gradient feature selection
MUC_DATA_SHEET
- C8051F340/1/2/3/4/5/6/7 devices are fully integrated mixed-signal System-on-a-Chip MCUs. Highlighted features are listed below. Refer to Table 1.1 for specific product feature selection
ARAC
- 语音识别中的语音信号特征提取和选择是关系到语音识别模型性能的一个重要问题。音信号的特征提取是解决时域信号的数字表问题，特征选择则是在多个特征中选择有的特征为后继的模式划分部分提供数据。-Speech recognition in speech signal feature extraction and selection in relation to the performance of speech recognition model of an important issue. Audio
Output
- a complete paper GA based feature selection
TextFeatureSelectionAlgorithm
- 在文本挖掘中，文档通常以特征向量的形式表示。为了提高文本挖掘算法的运行速度，降低占用的内存空间，过滤掉不相关或相关程度低的特征，提出一种改进的特征选择算法，该算法对特征进行综合考虑，从而更加准确地选取有效的特征。实验验证了改进算法的可行性和有效性。-In text mining，documents are usually meanwith the form of the eigenvector．In order to enhance the operating speed and red
master_thesis
- 音乐领域中文实体关系抽取研究 实体关系抽取的任务是从文本中抽取出两个或者多个实体之间预先定义 好的语义关系。本文将实体关系抽取定义为一个分类问题，主要研究内容是 中文音乐领域的实体关系抽取。针对这一问题，本文首先构建了中文音乐实 体关系语料库，然后分别采用了基于序列模式挖掘的无指导的方法和基于特 征提取的有指导的方法来解决这一问题。 -Dissertation for the Master Degree in Engineering urgently needed to de
Using-Temporal-Indicator-Functions-with-Generaliz
- A significant amount of research and practice in the law enforcement arena focuses on spatial and temporal event analysis. And although some efforts have been made to integrate spatial and temporal analysis, the majority of the previous work focu
ORDINAL-FEATURE-ANALYSIS
- 本文讨论了故障严重程度识别中的特征评价和特征选择算法。将严重程度识别问题中的特征分为非单调特征和单调特征，分别设计了不同的特征评价指标和特征选择算法，为故障严重程度识别提供了可行的方案。-This article discusses the failure to identify the severity of the feature in the comments and feature selection algorithms. The severity of the problem of
F4237084615
- Robust Optimal PSO based Wavelet Feature Selection in MIMO OFDM Systems
Feature-Selection-on-Classification-of-Medical-Da
- Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization
randomized-dimensionality-reduction-for-k-means.r
- Randomized Dimensionality Reduction for k-means Clustering This paper makes further progress towards a better understanding of dimensionality reduction for kmeans clustering. Namely, we present the first provably accurate feature selection met