会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 3. 发明授权
    • Semi-supervised learning based on semiparametric regularization
    • 基于半参数正则化的半监督学习
    • US08527432B1
    • 2013-09-03
    • US12538849
    • 2009-08-10
    • Zhen GuoZhongfei (Mark) Zhang
    • Zhen GuoZhongfei (Mark) Zhang
    • G06F15/18G06E1/00G06E3/00G06G7/00
    • G06N99/005
    • Semi-supervised learning plays an important role in machine learning and data mining. The semi-supervised learning problem is approached by developing semiparametric regularization, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. A semi-supervised learning approach is provided which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermore, the proposed approach which naturally extends to the out-of-sample data is an inductive learning method in nature.
    • 半监督学习在机器学习和数据挖掘中起着重要的作用。 半监督学习问题是通过开发半参数正则化来实现的,它试图通过利用数据的几何分布来发现数据的边缘分布来学习参数函数。 然后将该学习的参数函数作为现有知识并入到可用标记数据的监督学习中。 提供了一种半监督学习方法,其通过从包括标记和未标记数据的整个数据中学习的参数函数将未标记的数据合并到监督学习中。 参数函数反映数据边际分布的几何结构。 此外,自然地扩展到样本外数据的提出的方法本质上是归纳学习方法。
    • 4. 发明授权
    • Enhanced max margin learning on multimodal data mining in a multimedia database
    • 增强多媒体数据库中多模态数据挖掘的最大利润率学习
    • US08463053B1
    • 2013-06-11
    • US12538845
    • 2009-08-10
    • Zhen GuoZhongfei (Mark) Zhang
    • Zhen GuoZhongfei (Mark) Zhang
    • G06K9/62
    • G06F17/30256G06F17/10G06F17/30G06F17/30017G06K9/62G06K9/6218G06K9/6269G06K9/629
    • Multimodal data mining in a multimedia database is addressed as a structured prediction problem, wherein mapping from input to the structured and interdependent output variables is learned. A system and method for multimodal data mining is provided, comprising defining a multimodal data set comprising image information; representing image information of a data object as a set of feature vectors in a feature space; clustering in the feature space to group similar features; associating a non-image representation with a respective image data object based on the clustering; determining a joint feature representation of a respective data object as a mathematical weighted combination of a set of components of the joint feature representation; optimizing a weighting for a plurality of components of the mathematical weighted combination with respect to a prediction error between a predicted classification and a training classification; and employing the mathematical weighted combination for automatically classifying a new data object.
    • 在多媒体数据库中的多模式数据挖掘被解决为结构化预测问题,其中从输入到结构化和相互依赖的输出变量的映射被学习。 提供了一种用于多模式数据挖掘的系统和方法,包括定义包括图像信息的多模式数据集; 将数据对象的图像信息表示为特征空间中的一组特征向量; 聚类在特征空间中组合相似特征; 基于聚类将非图像表示与相应的图像数据对象相关联; 确定相应数据对象的联合特征表示作为所述联合特征表示的一组分量的数学加权组合; 针对预测分类和训练分类之间的预测误差优化数学加权组合的多个分量的权重; 并采用数学加权组合来自动分类新的数据对象。
    • 8. 发明授权
    • Reference voltage generation circuit and method
    • 参考电压发生电路及方法
    • US08766611B2
    • 2014-07-01
    • US13205324
    • 2011-08-08
    • Min-Hung HuChen-Tsung WuZhen-Guo DingPin-Han Su
    • Min-Hung HuChen-Tsung WuZhen-Guo DingPin-Han Su
    • G05F3/26G05F1/577
    • G05F3/242G05F1/561G05F3/30Y10T307/406
    • A reference voltage generation circuit includes: a bandgap reference circuit, generating a plurality of initial currents with different temperature coefficients; a base voltage generation circuit, combining the initial current into a combined current, and converting the combined current into one or more base voltages; a bias current source circuit, generating one or more bias currents based on at least one of the initial currents; and one or more regulating output circuit, each converting a respective one of the one or more bias currents into an increment voltage and adding the increment voltage to the base voltage to generate a respective output voltage. Each output voltage may have its respective temperature coefficient.
    • 参考电压产生电路包括:带隙参考电路,产生具有不同温度系数的多个初始电流; 基极电压产生电路,将初始电流组合成组合电流,并将组合电流转换成一个或多个基极电压; 偏置电流源电路,基于所述初始电流中的至少一个产生一个或多个偏置电流; 以及一个或多个调节输出电路,每个将一个或多个偏置电流中的相应一个转换为增量电压,并将增量电压加到基极电压以产生相应的输出电压。 每个输出电压可以具有各自的温度系数。
    • 10. 发明授权
    • Knowledge discovery from citation networks
    • 引文网络的知识发现
    • US08630975B1
    • 2014-01-14
    • US13310098
    • 2011-12-02
    • Zhen GuoMark Zhang
    • Zhen GuoMark Zhang
    • G06F7/00G06F17/30
    • G06N99/005G06F17/30011G06F17/30525G06F17/3053G06F17/30882G06N7/005G06Q10/10
    • In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. A Bernoulli Process Topic (BPT) model is provided which models the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of the citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach.
    • 在诸如数字图书馆等科学文章的语料库中,文件通过引用连接,一个文件在语料库中起着两个不同的作用:文档本身和其他文件的引用。 提供了一个伯努利流程主题(BPT)模型,它在两个层次上对语料库进行建模:文档级别和引文级别。 在BPT模型中,每个文档在与其角色相关联的潜在主题空间中具有两个不同的表示。 此外,引用网络的多层次分层结构是由涉及伯努利过程的生成过程所捕获的。 BPT模型的分布参数通过变分近似法估计。