1.
Construction Method for Training Data Set in Classification Algorithm of Support Vector Machines
支持向量机分类算法中训练样本集的构造方法
2.
The Selection of Classify Attribute from Web Page Training-set Base on Rough Sets;
基于粗糙集的网页训练样本集的分类属性的选择
3.
New reduction strategy of large-scale training sample set for SVM
一种新的支持向量机大规模训练样本集缩减策略
4.
Construction of the Training Data Set in Intrusion Detection Systems Based on the Agglomerate Clustering Algorithm
入侵检测系统中基于凝聚聚类算法的训练样本集的构造
5.
The Training Strategy of SMO Used for the Large-scale Data;
针对大规模样本集的SMO训练策略
6.
Sample Reduction Strategy for SVM Large-scale Training Data Set Using PSO
利用粒子群算法缩减大规模数据集SVM训练样本
7.
They are more efficient than inductive SVMs, especially for very small training sets and large test sets.
它在包含少量有标签样本的训练集和大量无标签样本的测试集上,具有良好的效果。
8.
Research on Face Recognition with Single Training Image Per Person
单训练样本条件下人脸识别技术研究
9.
A Chinese Text Classification System Based on Dynamic Training Data Set
一种动态调整训练集的中文文本分类系统
10.
Face Recognition Researches on Large Scale and Each Person with Few Samples;
大类别及少量训练样本的人脸识别问题研究
11.
Signature Verification Incorporating the Prior Model;
结合先验模型、无简单伪造训练样本的签名鉴定
12.
Selection of Training Samples for SVM Based on AdaBoost Approach
基于AdaBoost方法的支持向量机训练样本选择
13.
Quantitative Measurement of Training Sample Capacity for Chinese Statistical Language Model
汉语统计语言模型训练样本容量的定量化度量
14.
Face recognition from single sample per class based on Gabor filtering
基于Gabor变换的每类单个训练样本人脸识别研究
15.
A Method for Reducing the Amount of Training Samples in KNN Text Classification Based on Clustering and Density
基于聚类和密度的KNN分类器训练样本约减方法
16.
BP Algorithm Improvement Based on Sample Expected Training Number
基于样本期望训练数的BP神经网络改进研究
17.
The training is the same for a Zen warrior, for a Samurai -- the same.
这种训练与禅宗的战士、日本武士一样——这是一样的。
18.
The present study hypothesized that training in an individual event will cultivate the independent self whereas training in a team event will cultivate the interdependent self.
本研究假设,个人项目的训练培养独立自我倾向,集体项目的训练培养互联自我倾向。