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混合型学习
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  mixed mode study
     Theory and Practice of "Guiding and Learning"of Collaborative Teaching Model and Mixed Mode Study
     “导—学”协同教学模式和混合型学习的理论与实践
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  “混合型学习”译为未确定词的双语例句
     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
短句来源
     Study on Blended Learning Environment Construction in Traditional University
     论传统大学“混合型学习”环境的建设
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     The incremental algorithm IHMCAP successfully settles the dilemma of learning accuracy proportion between symbolic and neural parts by adopting neural network algorithm FTART which fits for hybrid learning. It also has the incremental learning abllity of adjusting old structures to improve learning accuracy by one time learning instead of rebuilding the decision tree and the neural networks when new examples are provided.
     增量式IHMCAP算法采用适合于混合型学习的FTART神经网络,成功解决了符号学习与神经网络学习精度之间的均衡性问题.该算法还具有较强的增量学习能力,在给系统增加新的示例时,不用重新生成已有判定树和神经网络,只需进行一遍增量学习即可调整原结构以提高学习精度,效率高,速度快.
短句来源
     Conclusion:Phonological recoding deficit in lexical access was a potential cause of the verbal learning disability or mixed learning disability.
     结论:语音解码缺陷可能是语文学习障碍或混合型学习障碍的原因之一。
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  相似匹配句对
     Study on Blended Learning Environment Construction in Traditional University
     论传统大学“混合型学习”环境的建设
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     learning synchronically;
     同步学习;
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     A Summary on Reinforcement Learning
     加强学习
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     (4)Mixed type.
     ④混合型
短句来源
     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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  mixed mode study
The external category therefore includes most students undertaking e-learning as well as distance education by paper-based and mixed mode study.
      
  blending learning
Blending Learning is not a simple mixed solution towards specific problems, but a very personalized and creative work.
      
In the following section, we describe two blending learning environments that have been designed using mixed and virtual reality technologies.
      


The multi-concept acquisition algorithm HMCAP and the neural network algorithm FTART are integrated in hybrid learning model HLM, which can deal with multiple concepts and continuous attributes. In this paper, the incremental learning algorithm of HLM which based on the structure of hybrid binary decision tree and FTART network is proposed. It has the ability of adjusting old structure to improve learning accuracy by once learning instead of rebuilding the decision tree and the neural networks when the new...

The multi-concept acquisition algorithm HMCAP and the neural network algorithm FTART are integrated in hybrid learning model HLM, which can deal with multiple concepts and continuous attributes. In this paper, the incremental learning algorithm of HLM which based on the structure of hybrid binary decision tree and FTART network is proposed. It has the ability of adjusting old structure to improve learning accuracy by once learning instead of rebuilding the decision tree and the neural networks when the new examples were provided.

混合型学习模型HLM将概念获取算法HMCAP和神经网络算法FTART有机结合,能学习多概念和连续属性,其增量学习算法建立在二叉混合判定树结构和FTART网络的基础上,在给系统增加新的实例时,只需进行一遍增量学习调整原结构,不用重新生成判定树和神经网络,即可提高学习精度,速度快、效率高.本文主要介绍该模型中的增量学习算法.

The incremental algorithm IHMCAP successfully settles the dilemma of learning accuracy proportion between symbolic and neural parts by adopting neural network algorithm FTART which fits for hybrid learning. It also has the incremental learning abllity of adjusting old structures to improve learning accuracy by one time learning instead of rebuilding the decision tree and the neural networks when new examples are provided.

增量式IHMCAP算法采用适合于混合型学习的FTART神经网络,成功解决了符号学习与神经网络学习精度之间的均衡性问题.该算法还具有较强的增量学习能力,在给系统增加新的示例时,不用重新生成已有判定树和神经网络,只需进行一遍增量学习即可调整原结构以提高学习精度,效率高,速度快.

Objective:To explore phonological recoding of learning Disabled children in lexical access and its relation to learning disabilities.Methods:Ninety-seven learning disabled children (31 Verbal disabled, 21 Mathematic disabled, and 45 Mixed disabled) and 63 nondisabled children were compared on Automatic counting, Chinese word-reading, Picture-naming, and Story-reading. Results:Except for picture-naming, the verbal disabled and mixed disabled children were worse than the control and mathematic disabled children...

Objective:To explore phonological recoding of learning Disabled children in lexical access and its relation to learning disabilities.Methods:Ninety-seven learning disabled children (31 Verbal disabled, 21 Mathematic disabled, and 45 Mixed disabled) and 63 nondisabled children were compared on Automatic counting, Chinese word-reading, Picture-naming, and Story-reading. Results:Except for picture-naming, the verbal disabled and mixed disabled children were worse than the control and mathematic disabled children on the phonetic recoding tasks. Reliable correlation were obtained between the measures of phonological recoding and Chinese score.Conclusion:Phonological recoding deficit in lexical access was a potential cause of the verbal learning disability or mixed learning disability.

目的:考查学习障碍儿童的长时记忆解码功能及与学习障碍的关系。方法:对97名学习障碍儿童(语文障碍31人、数学障碍21人、混合型障碍45人)和63名正常学习儿童的语音解码功能作个别测查,测试内容包括数数、汉词朗读、图画命名和故事朗读。结果:语文障碍和混合型障碍组除图画命名外,其他三项任务的解码速度比对照组慢,错误数比对照组多;语文障碍组汉词和故事朗读速度也比数学障碍组慢,错误数多;数学障碍组的解码速度和准确性与对照组相比差异无显著性;解码速度和准确性仅与语文成绩相关。结论:语音解码缺陷可能是语文学习障碍或混合型学习障碍的原因之一。

 
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