专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US06415286B1 Computer system and computerized method for partitioning data for parallel processing 失效
标题翻译：用于并行处理分区数据的计算机系统和计算机化方法
公开(公告)号：US06415286B1
公开(公告)日：2002-07-02
申请号：US09281984
申请日：1999-03-29
申请人： Anthony Passera , John R. Thorp , Michael J. Beckerle , Edward S. Zyszkowski
发明人： Anthony Passera , John R. Thorp , Michael J. Beckerle , Edward S. Zyszkowski
IPC分类号： G06F15163
CPC分类号： G06F17/30224 , G06F9/5027 , G06K9/00973 , G06K9/6218 , G06K9/6282 , G06N3/0454 , Y10S707/99933 , Y10S707/99936 , Y10S707/99938
摘要： A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks. Each non-terminal node automatically may send a desired ratio of the training records it receives to each of its child nodes, so the leaf node networks each receives approximately the same number of training records. The system may automatically configures the tree to have a number of leaf nodes equal to the number of separate processors available to train leaf node networks. After the non-terminal and leaf node networks have been trained, the records of a large data base can be passed through the tree for classification or for estimation of certain parameter values.
摘要翻译：计算机系统将数据空间拆分为处理器或进程之间的数据分区。可以使用决策树将数据空间拆分成不需要与定义数据空间参数的轴正交的子区域。决策树可以在其非终端节点中的每个训练数据上进行训练并用于分割训练数据的神经网络。每个终端或叶节点可以具有对到达终端节点的训练数据训练的隐层神经网络。可以在一个处理器上执行非终端节点神经网络的训练，并且可以在单独的处理器上运行叶节点的神经网络的训练。不同目标值可用于不同非终端节点网络的训练。非终端节点网络可以是隐层神经网络。每个非终端节点可自动发送其接收到的每个子节点的培训记录的期望比例，因此叶节点网络每个接收大约相同数量的训练记录。该系统可以自动地配置该树以使得多个叶节点等于可用于训练叶节点网络的单独处理器的数量。在非终端和叶节点网络被训练之后，大数据库的记录可以通过树进行分类或估计某些参数值。

2. 发明申请

US20090112516A1 SYSTEM AND METHOD FOR RELATING INFORMATION ACROSS SEEMINGLY UNRELATED TOPICS 审中-公开
标题翻译：相关信息的系统和方法
公开(公告)号：US20090112516A1
公开(公告)日：2009-04-30
申请号：US11877892
申请日：2007-10-24
申请人： Ran Zilca , Peter Jason Rentfrow , Anthony Passera
发明人： Ran Zilca , Peter Jason Rentfrow , Anthony Passera
IPC分类号： G06F17/18
CPC分类号： G06Q30/02 , G06F16/9535
摘要： A system and method for determining likelihoods of relationships between unrelated variables associated with characteristics of a user includes collecting scores for a plurality of variables and transforming the scores to discrete values. A first property having a discrete value and a second property having a discrete value are selected. How many times more likely the first property is exhibited for people who have the second property as compared to a general probability in an entire population for the first property to be exhibited is represented by computing a ratio of probabilities. The ratio of probabilities is reported.
摘要翻译：用于确定与用户的特征相关联的不相关变量之间的关系的可能性的系统和方法包括收集多个变量的分数并将分数变换为离散值。选择具有离散值的第一属性和具有离散值的第二属性。通过计算概率的比例来表示第一属性对于具有第二属性的展示的可能性多于整个群体中用于展示第一属性的一般概率。报告概率的比率。

3. 发明授权

US5909681A Computer system and computerized method for partitioning data for parallel processing 失效
标题翻译：用于并行处理分区数据的计算机系统和计算机化方法
公开(公告)号：US5909681A
公开(公告)日：1999-06-01
申请号：US624844
申请日：1996-03-25
申请人： Anthony Passera , John R. Thorp , Michael J. Beckerle , Edward S. A. Zyszkowski
发明人： Anthony Passera , John R. Thorp , Michael J. Beckerle , Edward S. A. Zyszkowski
IPC分类号： G06F9/50 , G06F17/30 , G06K9/00 , G06K9/62 , G06N3/04 , G06F15/163
CPC分类号： G06F17/30224 , G06F9/5027 , G06K9/00973 , G06K9/6218 , G06K9/6282 , G06N3/0454 , Y10S707/99933 , Y10S707/99936 , Y10S707/99938
摘要： A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined by the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks. Each non-terminal node automatically may send a desired ratio of the training records it receives to each of its child nodes, so the leaf node networks each receives approximately the same number of training records. The system may automatically configures the tree to have a number of leaf nodes equal to the number of separate processors available to train leaf node networks. After the non-terminal and leaf node networks have been trained, the records of a large data base can be passed through the tree for classification or for estimation of certain parameter values.
摘要翻译：计算机系统将数据空间拆分为处理器或进程之间的数据分区。可以使用决策树将数据空间拆分成不需要与由数据空间参数定义的轴正交的子区域。决策树可以在其非终端节点中的每个训练数据上进行训练并用于分割训练数据的神经网络。每个终端或叶节点可以具有对到达终端节点的训练数据训练的隐层神经网络。可以在一个处理器上执行非终端节点神经网络的训练，并且可以在单独的处理器上运行叶节点的神经网络的训练。不同目标值可用于不同非终端节点网络的训练。非终端节点网络可以是隐层神经网络。每个非终端节点可自动发送其接收到的每个子节点的培训记录的期望比例，因此叶节点网络每个接收大约相同数量的训练记录。该系统可以自动地配置该树以使得多个叶节点等于可用于训练叶节点网络的单独处理器的数量。在非终端和叶节点网络被训练之后，大数据库的记录可以通过树进行分类或估计某些参数值。

4. 发明申请

US20090037521A1 System and method for identifying compatibility between users from identifying information on web pages 审中-公开
标题翻译：用于识别用户之间的兼容性的系统和方法，用于识别网页上的信息
公开(公告)号：US20090037521A1
公开(公告)日：2009-02-05
申请号：US11888974
申请日：2007-08-03
申请人： Ran Zilca , Ryan L. Osborn , Anthony Passera , David Rosenbloom
发明人： Ran Zilca , Ryan L. Osborn , Anthony Passera , David Rosenbloom
IPC分类号： G06F15/16 , G06F17/30 , G06F7/10
CPC分类号： G06Q30/08 , G06F16/9535
摘要： A system and method for discovering and displaying the compatibility between users of a search service is presented. The searching user registers with the search service and installs a web browser toolbar or component. The searching user browses web pages, and the toolbar searches each web page for identifying information. The toolbar then transmits the identifying information to a search server, which compares the identifying information with listed identifying information for other registered users. Upon finding one or more registered users corresponding to the identifying information, the search server then determines the compatibility between the searching user and each identified registered user, sending the compatibility reports back to the toolbar. The toolbar displays the compatibility reports to the searching user in the browser.
摘要翻译：提出了一种用于发现和显示搜索服务用户之间的兼容性的系统和方法。搜索用户向搜索服务注册并安装Web浏览器工具栏或组件。搜索用户浏览网页，并且工具栏搜索每个网页以识别信息。然后，工具栏将识别信息发送到搜索服务器，该搜索服务器将识别信息与其他注册用户的列出的识别信息进行比较。在找到与识别信息相对应的一个或多个注册用户时，搜索服务器然后确定搜索用户和每个识别的注册用户之间的兼容性，将兼容性报告发送回工具栏。工具栏会在浏览器中显示搜索用户的兼容性报告。

5. 发明授权

US06347310B1 Computer system and process for training of analytical models using large data sets 失效
标题翻译：使用大数据集的分析模型的计算机系统和过程
公开(公告)号：US06347310B1
公开(公告)日：2002-02-12
申请号：US09075730
申请日：1998-05-11
申请人： Anthony Passera
发明人： Anthony Passera
IPC分类号： G06F1518
CPC分类号： G06K9/6273 , G06K9/6282 , G06N3/08 , G06N99/005
摘要： A database often contains sparse, i.e., under-represented, conditions which might be not represented in a training data set for training an analytical model if the training data set is created by stratified sampling. Sparse conditions may be represented in a training set by using a data set which includes essentially all of the data in a database, without stratified sampling. A series of samples, or “windows,” are used to select portions of the large data set for phases of training. In general, the first window of data should be a reasonably broad sample of the data. After the model is initially trained using a first window of data, subsequent windows are used to retrain the model. For some model types, the model is modified in order to provide it with some retention of training obtained using previous windows of data. Neural networks and Kohonen networks may be used without modification. Models such as probabilistic neural networks, generalized regression neural networks, Gaussian radial basis functions, decision trees, including K-D trees and neural trees, are modified to provide them with properties of memory to retain the effects of training with previous training data sets. Such a modification may be provided using clustering. is Parallel training models which partition the training data set into disjoint subsets are modified so that the partitioner is trained only on the first window of data, whereas subsequent windows are used to train the models to which the partitioner applies the data in parallel.
摘要翻译：数据库通常包含稀疏（即，低于代表的）条件，如果训练数据集是通过分层抽样创建的，则训练数据集中的训练数据集可能不被表示。稀疏条件可以通过使用包含数据库中的基本上所有数据的数据集而在训练集中表示，而不进行分层抽样。一系列样本或“窗口”用于选择训练阶段的大数据集的部分。一般来说，第一个数据窗口应该是相当广泛的数据样本。在使用第一个数据窗口最初训练模型之后，使用随后的窗口来重新训练模型。对于某些模型类型，该模型进行了修改，以便为其提供使用先前的数据窗口获得的训练的一些保留。可以使用神经网络和Kohonen网络而无需修改。修改了诸如概率神经网络，广义回归神经网络，高斯径向基函数，包括K-D树和神经树在内的决策树等模型，为其提供了存储器的属性，以保留训练与以前的训练数据集的效果。可以使用聚类提供这样的修改。将训练数据集划分为不相交子集的并行训练模型被修改，使分割器仅在第一个数据窗口进行训练，而随后的窗口用于训练分割器并行应用数据的模型。

6. 发明授权

US06272449B1 Computer system and process for explaining behavior of a model that maps input data to output data 失效
标题翻译：用于解释将输入数据映射到输出数据的模型的行为的计算机系统和过程
公开(公告)号：US06272449B1
公开(公告)日：2001-08-07
申请号：US09102349
申请日：1998-06-22
申请人： Anthony Passera
发明人： Anthony Passera
IPC分类号： G06F1710
CPC分类号： G06K9/6232 , G06N3/02
摘要： The present invention provides a description of the behavior of a model that indicates the sensitivity of the model in subspaces of the input space and which indicates which dimensions of the input data are salient in subspaces of the input space. By implementing this description using a decision tree, the subspaces and their salient dimensions are both described and determined hierarchically. A sensitivity analysis is performed on the model to provide a sensitivity profile of the input space of the model according to sensitivity of outputs of the model to variations in data input to the model. The input space is divided into at least two subspaces according to the sensitivity profile. A sensitivity analysis is performed on the model to provide a sensitivity profile of each of the subspaces according to sensitivity of outputs of the model to variations in data input to the model.
摘要翻译：本发明提供了一种模型的行为的描述，该模型指示模型在输入空间的子空间中的灵敏度，并且指示输入数据的哪个维度在输入空间的子空间中是显着的。通过使用决策树实现该描述，子空间及其显着尺寸被分层地描述和确定。对模型执行灵敏度分析，以根据模型输出的灵敏度向模型输入的数据的变化提供模型输入空间的灵敏度分布。输入空间根据灵敏度分布被划分为至少两个子空间。对模型执行灵敏度分析，以根据模型输出的灵敏度向模型输入的数据变化提供每个子空间的灵敏度分布。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式