A fast incremental learning algorithm is proposed.
提出了一种快速、增量式的学习算法。
Presents an improved incremental learning algorithm based on KKT conditions.
提出了一种改进的基于KKT条件的增量学习算法。
An incremental learning algorithm using multiple support vector machines (SVMs) is proposed.
给出了使用多支持向量机进行增量学习的算法。
A new geometric fast incremental learning algorithm for support vector machines (SVM) was proposed.
提出了一种新的基于壳向量的增量式支持向量机快速学习算法。
In order to extend common incremental learning algorithms into a parallel computation setting, an incremental learning algorithm with multiple support vector machine classifiers is proposed.
为了将一般增量学习算法扩展到并行计算环境中，提出一种基于多支持向量机分类器的增量学习算法。
Incremental algorithm is one of the major algorithms for raising learning efficiency.
增量算法是提高学习效率的一个重要算法之一。
Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed.
基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
This paper presents an adaptive and iterative support vector machine regression algorithm (CAISVR) based on chunking incremental learning and decremental learning procedures.
文中基于块增量学习和逆学习过程，提出了自适应迭代回归算法。
This algorithm is fast, incremental learning, and it takes less support vectors!
本算法具有速度快、增量学习、使用的支持向量少等显著优点。
After analysis of em algorithm, we presented a new cooperative training algorithm based on incremental learning.
本文在分析了EM算法的基础上，提出了一种新的协同训练算法。
The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.
该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。
The experiments show that IBN-M algorithm can learn comparatively accurate network from the extremely large dataset. IBN-M is an interesting improvement for incremental learning Bayesian Network.
实验结果表明IBN-M算法在数据缺失下贝叶斯网络的增量学习中确实能够学出相对精确的网络模型，该算法也是对贝叶斯网络增量学习方面的一个必要的补充。
The experiments show that IBN-M algorithm can learn comparatively accurate network from the extremely large dataset. IBN-M is an interesting improvement for incremental learning Bayesian Network.
实验结果表明IBN-M算法在数据缺失下贝叶斯网络的增量学习中确实能够学出相对精确的网络模型，该算法也是对贝叶斯网络增量学习方面的一个必要的补充。
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