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51. 发明申请

US20080256009A1 System for temporal prediction 有权
标题翻译：时间预测系统
公开(公告)号：US20080256009A1
公开(公告)日：2008-10-16
申请号：US11786949
申请日：2007-04-12
申请人： Qin Jiang , Narayan Srinivasa
发明人： Qin Jiang , Narayan Srinivasa
IPC分类号： G06N3/02
CPC分类号： G06N3/0436
摘要： Described is a system for temporal prediction. The system includes an extraction module, a mapping module, and a prediction module. The extraction module is configured to receive X(1), . . . X(n) historical samples of a time series and utilize a genetic algorithm to extract deterministic features in the time series. The mapping module is configured to receive the deterministic features and utilize a learning algorithm to map the deterministic features to a predicted {circumflex over (x)}(n+1) sample of the time series. Finally, the prediction module is configured to utilize a cascaded computing structure having k levels of prediction to generate a predicted {circumflex over (x)}(n+k) sample. The predicted {circumflex over (x)}(n+k) sample is a final temporal prediction for k future samples.
摘要翻译：描述了一种用于时间预测的系统。该系统包括提取模块，映射模块和预测模块。提取模块被配置为接收X（1），。。。 X（n）时间序列的历史样本，并利用遗传算法提取时间序列中的确定性特征。映射模块被配置为接收确定性特征并利用学习算法将确定性特征映射到时间序列的预测x（n + 1）样本。最后，预测模块被配置为利用具有k级预测的级联计算结构来生成预测的x（n + k）样本。预测的x（n + k）样本是k个未来样本的最终时间预测。

52. 发明申请

US20060129843A1 Method and apparatus for electronically extracting application specific multidimensional information from documents selected from a set of documents electronically extracted from a library of electronically searchable documents 审中-公开
公开(公告)号：US20060129843A1
公开(公告)日：2006-06-15
申请号：US11198798
申请日：2005-08-05
申请人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
发明人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
IPC分类号： G06F12/14
CPC分类号： G06F16/335 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： An apparatus and method is disclosed for providing application specific multi-dimensional information to an application running on a user computing device, wherein at least one dimension of the information is a category, from a plurality of member documents electronically extracted from a library of electronically searchable documents, which may comprise an application specific multidimensional information extractor adapted to extract occurrences of prospective representations of dimensions of application specific multidimensional information from the member documents, and to extract occurrences of non-application specific multidimensional information from the member documents; and, an encoder adapted to encode the occurrences of prospective dimensions of application specific multidimensional information and non-application specific multidimensional information contained in member documents according to a dimension specific coded representation of each dimension of application specific multidimensional information and a non-application specific coded representation of each non-application specific multidimensional information element. The apparatus and method may further comprise a member document identifier adapted to determine whether a member document contains coded formatting, and if not, whether the member document is a dense document, and if not, for rejecting the document from further processing, and the coded formatting may comprise network markup language coding. The apparatus and method may further comprise an application specific multidimensional information verification unit adapted verify the extraction of application specific multi-dimensional information from the member documents, and may further comprise a database for storing the application specific multi-dimensional information adapted to provide an application running on a user computing device access to the application specific multidimensional information. The application specific multidimensional information may be scheduled events having the dimensions of time, location and event identity, and the application running on the user computer can be an electronic calendar or other similar scheduling software program.

53. 发明授权

US06456991B1 Classification method and apparatus based on boosting and pruning of multiple classifiers 失效
标题翻译：基于多分类器的增强和修剪的分类方法和装置
公开(公告)号：US06456991B1
公开(公告)日：2002-09-24
申请号：US09388858
申请日：1999-09-01
申请人： Narayan Srinivasa , Yuri Owechko
发明人： Narayan Srinivasa , Yuri Owechko
IPC分类号： G06N302
CPC分类号： G06K9/6222 , G06N3/0409 , G06N3/0454 , G06N3/082
摘要： A boosting and pruning system and method for utilizing a plurality of neural networks, preferably those based on adaptive resonance theory (ART), in order to increase pattern classification accuracy is presented. The method utilizes a plurality of N randomly ordered copies of the input data, which is passed to a plurality of sets of booster networks. Each of the plurality of N randomly ordered copies of the input data is divided into a plurality of portions, preferably with an equal allocation of the data corresponding to each class for which recognition is desired. The plurality of portions is used to train the set of booster networks. The rules generated by the set of booster networks are then pruned in an intra-booster pruning step, which uses a pair-wise Fuzzy AND operation to determine rule overlap and to eliminate rules which are sufficiently similar. This process results in a set of intra-booster pruned booster networks. A similar pruning process is applied in an inter-booster pruning process, which eliminates rules from the intra-booster pruned networks with sufficient overlap. The final, derivative booster network captures the essence of the plurality of sets of booster networks and provides for higher classification accuracy than available using a single network.
摘要翻译：提出了一种用于利用多个神经网络，优选基于自适应共振理论（ART）的神经网络的增强和修剪系统和方法，以便增加模式分类精度。该方法利用输入数据的多个N个随机排列的副本，其被传递到多组增强网络。输入数据的多个N个随机排列的副本中的每一个被分成多个部分，优选地具有与期望识别的每个类对应的数据的相等分配。多个部分用于训练该组增强网络。然后由增强器网络组生成的规则在促进器内修剪步骤中被修剪，其使用成对的模糊AND操作来确定规则重叠并消除足够相似的规则。该过程导致一组内部加速器修剪的增强网络。一个类似的修剪过程被应用在增强器之间的修剪过程中，该过程消除了具有足够重叠的内部加速器修剪的网络的规则。最终的衍生增强网络捕获多组增强网络的本质，并提供比使用单个网络可用的更高的分类精度。

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