Thirdly, Learning Classifier system is applied to multi-robot system.
第三，研究了学习分类器系统在多机器人学习中的应用。
To accelerate the speed of Learning Classifier System, Rule Constructor and Merge operation are introduced.
为了提高该方法的收敛速度，本文引入了规则构造器和合并操作。
The algorithm used weighted templates to structure each weak learning classifier, which overcame the shortcoming of structuring classifier by using a single feature.
在该演化算法中，采取训练正反类样本加权模板的方法来构造各个弱学习分类器，克服了常规的基于单一特征构造弱分类器的不足。
The experiments on UCI Machine Learning Repository prove that, compared to existing measures, EPD shows stronger ability in predicting the performance of multiple classifier systems.
对UCI机器学习数据库的实验证明，相对于其它方法，EPD方法对多分类器系统性能的预测能力更强。
Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.
实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。
Learning rules are constructed according to deterministic annealing to optimize classifier parameters, on purpose to reduce classification error and system entropy of the space to be identified.
由确定性退火技术构造学习规则用于优化分类器参数，目的是减少分类误差以及待识别空间的系统熵。
For the support vector machine based learning algorithm of classifier, it is very importance for the support vector to be pre-selected.
在基于支撑矢量机的分类器学习算法中，预先选择支撑矢量是非常重要的。
The manual neural network has become more and more important as a classifier. Learning from the environment adaptively and generalizing were the most advantages of neural network.
人工神经网络日渐成为一种重要的分类工具，其最大益处就在于它善于对环境的自适应学习，并且具有并行处理泛化能力。
Efficient extraction of image texture features are used on the following support vector machine classifier learning and training have a very important role.
图像纹理特征的有效提取对下面所用到的支持向量机分类器来进行学习和训练有非常重要的作用。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
然而，传统的监督学习算法需要标记大量的训练样本来建立满意的分类器。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
Feature selection is an important issue in the fields of machine learning and pattern recognition. The effectiveness of feature directly affects the design and performance of the classifier.
特征选择问题是机器学习和模式识别中的一个重要问题，特征的优劣直接影响分类器的设计和性能。
ICF can classify unknown samples as the traditional classifier. It also has some functions such as multi-experts decision, pre-classifying and learning.
智能分类器不但可以对未知样本进行分类识别，还具有多专家决策、预分类、学习等功能。
Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.
半分类学习-将标签与非标签用例劫后生成一个合适的函数或分类器。
The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved.
并且采用了最小二乘支持向量机，用等式约束取代了支持向量机中的不等式约束，降低了运算量，提高了学习效率。
A new QBC method is presented by combining vote entropy and class conditional posterior maximum entropy for learning TAN classifier.
同时提出了基于投票熵与类条件后验最大熵相结合的QBC算法。
The classifier which was set up by the TANC - BIC structure - learning algorithm bad acquired success, but it didn't consider the class node.
用TANC—BIC结构学习算法构建的分类器取得了成功，但TANC—BIC结构学习算法未考虑类节点的情况。
This method utilizing co-learning among several classifiers, selects the unlabeled samples which have high confidence, and then refines each classifier with these samples.
该方法通过几个分类器间协同学习，选出标记可信度比较高的无标记数据，再利用这些数据对已有的分类器作进一步的改进。
This method utilizing co-learning among several classifiers, selects the unlabeled samples which have high confidence, and then refines each classifier with these samples.
该方法通过几个分类器间协同学习，选出标记可信度比较高的无标记数据，再利用这些数据对已有的分类器作进一步的改进。
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