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1. 发明授权

US6101275A Method for finding a best test for a nominal attribute for generating a binary decision tree 失效
标题翻译：找到用于生成二进制决策树的名义属性的最佳测试的方法
公开(公告)号：US6101275A
公开(公告)日：2000-08-08
申请号：US13147
申请日：1998-01-26
申请人： Don Coppersmith , Se June Hong , Jonathan R. M. Hosking
发明人： Don Coppersmith , Se June Hong , Jonathan R. M. Hosking
IPC分类号： G06K9/62 , G06K9/36 , G06K9/46 , G06K9/68
CPC分类号： G06K9/6282
摘要： A fast way for determining the best subset test for a nominal attribute in a decision tree. When a nominal attribute has n distinct values, the prior art requires computing the impurity functions on each of the 2.sup.n-1 -1 possible subset partitioning of the n values and finding the minimum case among them. This invention guarantees the minimum impurity test on the attribute by computing only (n-1) impurity function computations. This reduction of computational complexity makes it practically possible to find the true best tests for many real data mining application, where a binary decision tree is used as the classification model.
摘要翻译：确定决策树中名义属性的最佳子集测试的快速方法。当标称属性具有n个不同的值时，现有技术需要在n个值的2n-1-1个可能子集划分中的每一个上计算杂质函数，并且找出其中的最小值。本发明通过仅计算（n-1）杂质函数计算来保证对属性的最小杂质测试。这种计算复杂度的降低使得实际上可以为许多实际数据挖掘应用找到真正的最佳测试，其中使用二进制决策树作为分类模型。

2. 发明授权

US4241307A Module interconnection testing scheme 失效
标题翻译：模块互连测试方案
公开(公告)号：US4241307A
公开(公告)日：1980-12-23
申请号：US934936
申请日：1978-08-18
申请人： Se June Hong
发明人： Se June Hong
IPC分类号： G01R31/28 , G01R31/02 , G01R31/04 , G01R31/3185
CPC分类号： G01R31/318505 , G01R31/024 , G01R31/041
摘要： This specification describes the testing of interconnections between modules mounted on a card and between the modules and the input and output terminals of the card. Each of the modules has an Exclusive-OR circuit which receives an input from each of the input pins of the module and has a single output which is taken off an output pin of the module. Also, each of the modules has a test input circuit for accessing all of the output pins of the module in parallel from a single input terminal. The test input circuits are used to apply a binary 0 followed by a binary 1 to all the outputs of all the modules. The Exclusive-OR circuits are used to monitor the response to those signals. By testing in this manner, all the connections between the modules and also between the modules and the card terminals can be checked for stuck ones and zeros. In the preferred embodiment a more complex but still relatively simple bit pattern can test all the interconnection nets to determine if there are shorts between any of the nets.
摘要翻译：本规范描述了安装在卡上的模块和模块与卡的输入和输出端子之间的互连的测试。每个模块具有异或电路，其接收来自模块的每个输入引脚的输入，并且具有从模块的输出引脚取出的单个输出。此外，每个模块具有用于从单个输入端子并行访问模块的所有输出引脚的测试输入电路。测试输入电路用于将二进制0和二进制1应用于所有模块的所有输出。异或电路用于监视对这些信号的响应。通过以这种方式进行测试，可以检查模块之间以及模块和卡终端之间的所有连接，以确定卡住的零和零。在优选实施例中，更复杂但仍然相对简单的位模式可以测试所有互连网络，以确定任何网络之间是否存在短路。

3. 发明授权

US06546379B1 Cascade boosting of predictive models 失效
标题翻译：级联提升预测模型
公开(公告)号：US06546379B1
公开(公告)日：2003-04-08
申请号：US09427064
申请日：1999-10-26
申请人： Se June Hong , Barry K. Rosen
发明人： Se June Hong , Barry K. Rosen
IPC分类号： G06F1518
CPC分类号： G06K9/6256 , G06N5/025 , G06N99/005
摘要： A method of boosting of predictive models, called cascade boosting, for resolving the interpretability problem of previous boosting methods while mitigating the fragmentation problem when applied to decision trees. This method of cascade boosting always applies a single weak model to any given data point. An improvement to the common method of boosting lies in how weak models are organized in a decision list rather than a weighted average. Cascade boosting resolves the interpretability problem of previous boosting methods while mitigating the fragmentation problem when applied to decision trees. Cascade boosting is simplest when applied to segmented predictive models but may also be applied to predictive models that do not explicitly segment the space of possible data points. The predictive model resulting from cascade boosting has fewer rules, or tree leaves, thereby enabling a modeler to better understand the correlations among the data.
摘要翻译：一种提高预测模型的方法，称为级联升压，用于解决先前的升压方法的可解释性问题，同时在应用于决策树时减轻分段问题。这种级联升压的方法总是将单个弱模型应用于任何给定的数据点。普遍提升方法的改进在于决策列表中组织的模型薄弱，而不是加权平均数。级联提升解决了以前的提升方法的可解释性问题，同时在应用于决策树时减轻了碎片问题。级联升压在应用于分段预测模型时最简单，但也可应用于未明确分割可能数据点空间的预测模型。由级联提升产生的预测模型具有较少的规则或树叶，从而使建模者能够更好地了解数据之间的相关性。

4. 发明授权

US5652829A Feature merit generator 失效
标题翻译：特征优点发生器
公开(公告)号：US5652829A
公开(公告)日：1997-07-29
申请号：US281191
申请日：1994-07-26
申请人： Se June Hong
发明人： Se June Hong
IPC分类号： G06F9/44 , G06K9/62 , G06N5/04 , G06F17/00
CPC分类号： G06K9/6282
摘要： The invention discloses steps to compute a new contextual merit measure for feature variables based on a two-fold discovery. First, a feature variable does not exist by itself in distinguishing classes, and second, what is desired is to obtain its correlation to a class in the context of other feature variables. The invention specifies a merit measure that captures the contextual correlation implicitly.
摘要翻译：本发明公开了基于两重发现来计算特征变量的新的语境优点度量的步骤。首先，特征变量本身不存在于区分类中，其次，期望的是在其他特征变量的上下文中获得与类的相关性。本发明规定了隐含地捕获上下文相关性的优点度量。

5. 发明授权

US07020593B2 Method for ensemble predictive modeling by multiplicative adjustment of class probability: APM (adjusted probability model) 失效
标题翻译：通过乘法调整类概率进行集体预测建模的方法：APM（调整概率模型）
公开(公告)号：US07020593B2
公开(公告)日：2006-03-28
申请号：US10309191
申请日：2002-12-04
申请人： Se June Hong , Jonathan R. Hosking , Ramesh Natarajan
发明人： Se June Hong , Jonathan R. Hosking , Ramesh Natarajan
IPC分类号： G06F15/18
CPC分类号： G06K9/6217
摘要： A new method is used to model the class probability from data that is based on a novel multiplicative adjustment of the class probability by a plurality of items of evidence induced from training data. The optimal adjustment factors from each item of evidence can be determined by several techniques, a preferred embodiment thereof being the method of maximum likelihood. The evidence induced from the data can be any function of the feature variables, the simplest of which are the individual feature variables themselves. The adjustment factor of an item of evidence Ej is given by the ratio of the conditional probability P(C|Ej) of the class C given Ej to the prior class probability P(C), exponentiated by a parameter aj. The method provides a new and useful way to aggregate probabilistic evidence so that the final model output exhibits a low error rate for classification, and also gives a superior lift curve when distinguishing between any one class and the remaining classes. A good prediction for the class response probability has many uses in data mining applications, such as using the probability to compute expected values of any function associated with the response, and in many marketing applications where lift curves are generated for selected prioritized target customers.
摘要翻译：一种新的方法用于从基于从训练数据引发的多个证据项的类概率的新颖的乘法调整的数据来建模类概率。来自每个证据的最佳调整因子可以通过几种技术来确定，其优选实施方式是最大可能性的方法。从数据引发的证据可以是特征变量的任何函数，其中最简单的是个体特征变量本身。证据项目的调整因子E SUB由给定的类别C的条件概率P（C | E> j j）的比率给出与以前的类概率P（C）进行比较，由参数α取幂。该方法提供了一种新的有用的方法来聚合概率证据，使得最终模型输出表现出低的分类误差率，并且在区分任何一个类别和其余类别时也提供了优异的升力曲线。类反应概率的良好预测在数据挖掘应用中有许多用途，例如使用概率来计算与响应相关联的任何函数的预期值，以及在为选定的优先级目标客户生成升降曲线的许多营销应用中。

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