针对现有多标签数据集转换方法无法有效利用标签间的语义相关性和共现性知识,以及转换得到的数据集相对于问题规模偏小等问题,提出了一种新的多标签数据集转换方法 RAPC-W(ranking by all pairwise com-parision based WordNet)。该方...针对现有多标签数据集转换方法无法有效利用标签间的语义相关性和共现性知识,以及转换得到的数据集相对于问题规模偏小等问题,提出了一种新的多标签数据集转换方法 RAPC-W(ranking by all pairwise com-parision based WordNet)。该方法将标签对从原来的两对扩展到四对,增加了划分后数据集的规模。另外,引入了外部数据源WordNet,较好地考虑了标签语义相关性和共现性知识,一定程度上过滤掉了语义不相关的标签组合,更好地保留了原始数据集的信息,降低了噪声数据集对基分类器训练的不良影响。在UCI知识库提供的Yeast和Letter数据集以及KEEL提供的Emotion、Genbase数据集上的一系列实验结果表明,该方法是有效可行的。展开更多
The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inferenc...The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.展开更多
文摘针对现有多标签数据集转换方法无法有效利用标签间的语义相关性和共现性知识,以及转换得到的数据集相对于问题规模偏小等问题,提出了一种新的多标签数据集转换方法 RAPC-W(ranking by all pairwise com-parision based WordNet)。该方法将标签对从原来的两对扩展到四对,增加了划分后数据集的规模。另外,引入了外部数据源WordNet,较好地考虑了标签语义相关性和共现性知识,一定程度上过滤掉了语义不相关的标签组合,更好地保留了原始数据集的信息,降低了噪声数据集对基分类器训练的不良影响。在UCI知识库提供的Yeast和Letter数据集以及KEEL提供的Emotion、Genbase数据集上的一系列实验结果表明,该方法是有效可行的。
基金Supported by the National Science Foundation of China under Grant Nos. 10774150,10834014the China 973-Program under Grant Nos. 2007CB935903 and HKUST605010
文摘The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.
