incremental learning

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。

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Based on the out-of-sample framework and the graph embedding framework, the thesis presents a more general common framework for manifold learning. The new framework embodies graph construction, tensor learning, incremental learning and supervised learning.

提出新的流形学习统一框架在样本泛化与图嵌理论的基础上，提出了更一般的流形学习统一框架，这一框架包含了图的建立、张量学习、增量学习和有监督学习。

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Besides, based on hyper sphere SVMs, a new class incremental learning algorithm is presented.

同时，提出了一种新的类增量学习算法，该算法利用超球支持向量机进行分类。

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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。

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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。

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A weighted class incremental learning algorithm is presented, which improves the CIL algorithm. The algorithm adds the weighs of class to training samples.

提出了一种加权类增量学习算法，该算法是对CIL算法的改进，通过加入类权值，解决了因两类训练样本不平衡而造成的小类别分类精度较低的问题。

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Multi-class Incremental Learning Based on Support Vector Machines

基于支持向量机的多分类增量学习算法

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particle filter based infrared object tracking algorithm with incremental learning

基于增量学习的红外目标粒子滤波跟踪方法

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As an incremental learning system, Case-based reasoning ( CBR) makes the knowledge and rule obtainment bottleneck easy by learning from case, thus it has increasingly become a hotspot in the area of artificial intelligent.

基于事例的推理（CBR）作为一种增量式的学习方法，规避了传统人工智能在知识获取上的瓶颈问题，逐渐引起人工智能领域研究者的关注。

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based on the process of vision-creative ideas-action-result, incremental learning can be divided into three phases: adaptive learning, developmental learning and creative learning. accordingly, leapfrogging learning is composed of key-affair-learning, benchmarking learning, and unlearning.

渐进式学习基于愿景设想行动结果的流程，分为适应性学习、发展性学习和创造性学习三阶段；跨越式学习则由关键事件学习、标杆学习和忘却学习组成。

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When tagging a large number of categories with high price of the sample, combined with the incremental learning theory is an effective way to solve the problem.

当获得大量带有类别标注的样本代价较高时，与增量学习理论结合是解决问题的有效途径。

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dr. wang explained incremental learning and concept drift in the lecture.

王博士在讲座中解释了增量学习与概念漂移。

AI例句精选

to reduce the prediction time and improve predicting accuracy, an incremental learning algorithm, hs-svr, is proposed. this incremental learning algorithm can reduce the size of training set of prediction model.

为缩短预测时间并提高预测精度，本文提出HS-SVR增量学习算法，该增量学习算法用于缩减元素收得率模型的训练集样本数。

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The incremental learning algorithm in hybrid learning model HLM

混合型学习模型HLM中的增量学习算法

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research on incremental learning algorithm with multiple support vector machine classifiers

基于多支持向量机分类器的增量学习算法研究

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as we know, incremental learning is supported by k-nearest neighbor classification naturally, it is just to meet the requirements of updating the training sample set in the course of filtering spams.

K最近邻分类法自然支持增量学习的特性刚好能满足垃圾邮件过滤中要求训练集不断更新的要求。

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Model experiments show that, the financial distress prediction model based on the incremental learning system is stable and adaptable. Incremental prediction model outperforms other models, which is an effective financial distress prediction method.

财务数据实验表明，基于增量学习系统的财务危机预警模型兼具稳定性和适应性，模型预测性能优于滑动时间窗口模型，是一种有效的财务危机预警建模方法。

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