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   corner classification 的翻译结果: 查询用时:0.176秒
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corner classification
相关语句
  角分类
     Research on Forward Neural Network for Corner Classification
     角分类前向神经网络研究
短句来源
     Research on Forward Neural Network for Corner Classification Based on Real Vectors
     基于实向量输入的角分类前向神经网络研究
短句来源
     An Extended Corner Classification Neural Network Based Document Classification Approach
     基于扩展角分类神经网络的文档分类方法(英文)
短句来源
     Document classification approach by rough-set-based corner classification neural network
     一种基于粗糙集角分类神经网络的文档分类方法(英文)
短句来源
     CC4 (the 4th version of corner classification) neural network is a new type of corner classification training algorithm for three-layered feedforward neural networks. It has been provided as a document classification approach for metasearch engine Anvish.
     CC4神经网络是一种三层前馈网络的新型角分类(corner classification)训练算法,原用于元搜索引擎Anvish的文档分类.
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  相似匹配句对
     On Classification of
     ω_(2,2g/(3+2))~2的分类
短句来源
     ·classification.
     ①对地震层析成像方法进行分类.
短句来源
     Corner Application for Target Recognition and Classification
     角点在目标识别分类中的应用
短句来源
     Research on Forward Neural Network for Corner Classification
     角分类前向神经网络研究
短句来源
     Chinese Corner
     汉语角(英文)
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  corner classification
We present here a new and improved corner classification technique that retains the prescriptive learning capability and gives excellent generalization performance.
      
The corner classification approach to neural network training has the excellent capability ofprescriptive learning, where the network weights areprescribed merely by inspection of the training samples.
      
A new corner classification approach to neural network training
      
Another corner classification algorithm presented in this paper does not require any computations to find the weights.
      
Two corner classification algorithms are described.
      
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CC4 (the 4th version of corner classification) neural network is a new type of corner classification training algorithm for three-layered feedforward neural networks. It has been provided as a document classification approach for metasearch engine Anvish. On the condition that documents are almost of the same size, CC4 neural network is an effective document classification algorithm. However, when there is great difference in document sizes, CC4 neural network does not perform well. This...

CC4 (the 4th version of corner classification) neural network is a new type of corner classification training algorithm for three-layered feedforward neural networks. It has been provided as a document classification approach for metasearch engine Anvish. On the condition that documents are almost of the same size, CC4 neural network is an effective document classification algorithm. However, when there is great difference in document sizes, CC4 neural network does not perform well. This paper aims to extend the original CC4 neural network for effectively classifying documents having much difference in sizes. To achieve this goal, the authors propose a MDS-NN based data indexing method thus making all documents be mapped to k-dimensional points while their distance information is kept well. The authors also extend CC4 neural network so that it can accept k-dimensional indexes of documents as its input, then transform these indexes to binary sequences required by CC4 neural network. The experimental results show that the performance of ExtendedCC4 is much better than that of InitialCC4 when there is a great difference in document sizes. At the same time, the high classification precision of ExtendedCC4 has much relationship with the effectiveness of indexing methods.

CC4神经网络是一种三层前馈网络的新型角分类(corner classification)训练算法,原用于元搜索引擎Anvish的文档分类.当各文档之间的规模接近时,CC4神经网络有较好的分类效果.然而当文档之间规模差别较大时,其分类性能较差.针对这一问题,本文意图扩展原始CC4神经网络,达到对文档有效分类的效果.为此,提出了一种基于MDS-NN的数据索引方法,将每一文档映射至k维空间数据点,并尽可能多地保持原始文档之间的距离信息.其次,通过将索引信息变换为CC4神经网络接受的0,1序列,实现对CC4神经网络的扩展,使其能够接受索引信息作为输入.实验结果表明对相互之间规模差别较大的文档,扩展CC4神经网络的性能优于原始CC4神经网络的性能.同时,扩展CC4神经网络的分类精度与文档索引方法有密切关系.

To implement the personalization recommendation for text information retrieval (TR), a forward neural network, named as RealCC, with hybrid neuron for instant corner classification is presented and the user-focus is defined as the description for a user′s feature. If the RealCC network is trained with the user-focuses, the network can be used to calculate the recommending priority of each query result instantly. This paper gives an algorithm for training RealCC with user-focus. Experimental results show...

To implement the personalization recommendation for text information retrieval (TR), a forward neural network, named as RealCC, with hybrid neuron for instant corner classification is presented and the user-focus is defined as the description for a user′s feature. If the RealCC network is trained with the user-focuses, the network can be used to calculate the recommending priority of each query result instantly. This paper gives an algorithm for training RealCC with user-focus. Experimental results show that RealCC can instantly classify information and the implementation of personalization recommendation based on user-focus and lighten the user′s burden induced by valid filtering information while the time requirement of TR can be satisfied well.

为实现文本信息检索中的个性化推荐 ,本文以用户焦点作为用户个性特征的描述 ,设计了适用于快速分类的混合前向角分类神经元网络 Real CC。以用户焦点作为样本数据训练该网络后 ,可以通过该网络对用户查询结果进行快速分类以获得每条查询结果的推荐优先级。给出了使用用户焦点训练该网络的算法。实验表明 ,Real-CC可以在保持分类精度的同时快速的完成对数据的分类 ,同时 ,基于用户焦点的个性化推荐 ,可以有效地减轻用户因从包含大量无关信息的查询结果中筛选感兴趣信息而产生的负担 ,较好地满足了用户对文本信息检索的时间要求。

Instant classification is a hot area for researchers on online information retrieval and forward neural network is an important neural network for corner classification. This paper presents a new forward neural network consisting of two kinds of neurons. The topology, learning algorithm and behaviors of the network are given. Theoretical analysis shows that the time complexity of the network learning is linear. Experimental results indicate that ,compared with those consisting of binary neurons, the network...

Instant classification is a hot area for researchers on online information retrieval and forward neural network is an important neural network for corner classification. This paper presents a new forward neural network consisting of two kinds of neurons. The topology, learning algorithm and behaviors of the network are given. Theoretical analysis shows that the time complexity of the network learning is linear. Experimental results indicate that ,compared with those consisting of binary neurons, the network can improve classification precision remarkably while satisfying the time requirement satisfied consisting of binary neurons.

快速分类是在线信息检索研究者关注的热点 .角分类前向神经网络是一类快速分类神经网络 .给出了一个新型的由两种神经元混合构成的神经网络 ,定义了相应的网络拓扑、学习算法和动力学行为 .分析表明 ,该网络的学习的时间复杂度是线性的 .实验表明 ,与二值神经元构成的神经网络相比 ,该网络在满足快速分类要求的同时 ,其分类准确率有显著的提高

 
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