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公开(公告)号：US5704017A
公开(公告)日：1997-12-30
申请号：US602238
申请日：1996-02-16
CPC分类号： H04N21/252 , G06Q30/02
摘要： The disclosed system provides an improved collaborative filtering system by utilizing a belief network, which is sometimes known as a Bayesian network. The disclosed system learns a belief network using both prior knowledge obtained from an expert in a given field of decision making and a database containing empirical data obtained from many people. The empirical data contains attributes of users as well as their preferences in the field of decision making. After initially learning the belief network, the belief network is relearned at various intervals when additional attributes are identified as having a causal effect on the preferences and data for these additional attributes can be gathered. This relearning allows the belief network to improve its accuracy at predicting preferences of a user. Upon each iteration of relearning, a cluster model is automatically generated that best predicts the data in the database. After relearning the belief network a number of times, the belief network is used to predict the preferences of a user using probabilistic inference. In performing probabilistic inference, the known attributes of a user are received and the belief network is accessed to determine the probability of the unknown preferences of the user given the known attributes. Based on these probabilities, the preference most likely to be desired by the user can be predicted.
摘要翻译： 所公开的系统通过利用有时被称为贝叶斯网络的置信网络来提供改进的协同过滤系统。 所公开的系统使用从给定的决策领域的专家获得的现有知识和包含从许多人获得的经验数据的数据库来学习信念网络。 实证数据包含用户的属性以及决策领域的偏好。 在最初学习信念网络之后，当附加属性被识别为对偏好具有因果影响并且可以收集这些附加属性的数据时，信念网络以不同的间隔被重新学习。 这种再学习允许信念网络在预测用户的偏好时提高其准确性。 在重新学习的每次迭代之后，自动生成最能预测数据库中的数据的集群模型。 在重新学习信念网络多次之后，信念网络用于使用概率推理来预测用户的偏好。 在执行概率推理时，接收用户的已知属性，并且访问置信网络以确定给定已知属性的用户的未知偏好的概率。 基于这些概率，可以预测用户最可能希望的偏好。
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公开(公告)号：US5694559A
公开(公告)日：1997-12-02
申请号：US400797
申请日：1995-03-07
申请人： Samuel D. Hobson , Eric Horvitz , David E. Heckerman , John S. Breese , Erich-S.o slashed.ren Finkelstein , Gregory L. Shaw , James R. Flynn , Karen Jensen
发明人： Samuel D. Hobson , Eric Horvitz , David E. Heckerman , John S. Breese , Erich-S.o slashed.ren Finkelstein , Gregory L. Shaw , James R. Flynn , Karen Jensen
CPC分类号： G06F17/30672 , G06F17/30687 , G06F9/4446 , G06F9/4448 , Y10S707/99933
摘要： An improved free text query method and system is provided as part of an improved on-line help system. In a preferred embodiment of the present invention, the on-line help system provides a free text query system that performs partial analysis. The partial analysis performed by the preferred embodiment includes identifying keywords within input provided by the user, performing disambiguation analysis, performing definiteness analysis, performing capitalization analysis, and generating a ranked list of candidates according to a probability associated with each candidate. In addition, the preferred embodiment of the present invention is internationalizable. That is, the present invention is easily ported between different languages.
摘要翻译： 提供了一种改进的自由文本查询方法和系统，作为改进的在线帮助系统的一部分。 在本发明的优选实施例中，在线帮助系统提供执行部分分析的自由文本查询系统。 由优选实施例执行的部分分析包括识别由用户提供的输入内的关键字，执行消歧分析，执行确定性分析，执行大小写分析，以及根据与每个候选相关联的概率生成候选排序列表。 此外，本发明的优选实施例是可国际化的。 也就是说，本发明容易地在不同语言之间移植。
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公开(公告)号：US08706421B2
公开(公告)日：2014-04-22
申请号：US11738411
申请日：2007-04-20
摘要： Shift invariant predictors are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.
摘要翻译： 这里描述了移位不变量预测器。 作为示例，用于预测与蛋白质和配体的结合相关的结合信息的系统可以包括训练的结合模型和预测组分。 经训练的结合模型可以包括表示蛋白质结合位点处配体的未知比对的隐藏变量。 预测组件可以被配置为通过使用关于蛋白质序列，配体序列和训练的结合模型的信息来预测结合信息。
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84.发明授权公开(公告)号：US08478535B2
公开(公告)日：2013-07-02
申请号：US11324506
申请日：2005-12-30
IPC分类号： G01N33/50
摘要： The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.
摘要翻译： 本发明提供了通过诸如成本函数，贪心算法，期望最大化（EM）算法等机器学习算法来促进艾滋病疫苗鸡尾酒组合的系统和方法。可以利用这种装配来产生疫苗鸡尾酒， 在宿主免疫压力下快速发展的病原体。 例如，本发明的系统和方法可以用于促进用于诸如HIV的病原体的T细胞疫苗的设计。 此外，本发明的系统和方法可以与其他应用相结合使用，例如序列比对，基序发现，分类和重组热点检测。 本文所述的新颖技术可以提供改进，以通过构建具有较高表位覆盖度的疫苗混合物来设计疫苗的传统方法，例如与来自数据的共同体，树节点和随机菌株的鸡尾酒相比。
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85.发明授权Decision theoretic approach to targeted solicitation by maximizing expected profit increases 有权通过最大化预期利润增长的决策理论方法进行有针对性的招标公开(公告)号：US08103537B2
公开(公告)日：2012-01-24
申请号：US11257473
申请日：2005-10-24
IPC分类号： G06Q99/00
CPC分类号： G06Q30/02 , G06Q30/0201 , G06Q30/0202 , G06Q30/0203 , G06Q30/0204 , G06Q30/0207 , G06Q30/0236 , G06Q30/0247 , G06Q30/0251 , G06Q30/0273
摘要： A decision theoretic approach to targeted solicitation, by maximizing expected profit increases, is disclosed. A decision theoretic model is used to identify a sub-population of a population to solicit, where the model is constructed to maximize an expected increase in profits. A decision tree in particular can be used as the model. The decision tree has paths from a root node to a number of leaf nodes. The decision tree has a split on a solicitation variable in every path from the root node to each leaf node. The solicitation variable has two values, a first value corresponding to a solicitation having been made, and a second value corresponding to a solicitation not having been made.
摘要翻译： 披露了通过最大化预期利润增长的针对性招标的决策理论方法。 决策理论模型用于识别人口的子群体，以便建立模型以最大化利润的预期增长。 决策树特别可以用作模型。 决策树具有从根节点到多个叶节点的路径。 决策树在从根节点到每个叶节点的每个路径中的请求变量上都有一个拆分。 招标变量具有两个值，对应于已经作出的邀请的第一个值，以及对应于未作出的请求的第二个值。
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公开(公告)号：US08000900B2
公开(公告)日：2011-08-16
申请号：US11324634
申请日：2005-12-30
CPC分类号： G06F19/22 , C12Q1/6883 , C12Q2537/165 , G06F19/00 , G06F19/14 , G06F19/18 , G06F19/24 , G16H50/20 , Y02A90/26
摘要： A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism's condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism's condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.
摘要翻译： 这里描述了一种系统，其包括由主机和病原体之间的多个关联训练的机器学习分类器来预测病原体特征。 病原体特征可以与宿主的疾病状态有关。 本文还描述了用于执行预测影响生物体状况的目标分子的一部分的预测方法的计算机可执行指令。 该方法包括使用群体数据来自动分析目标分子的一个或多个区域以确定预期影响生物体状况的目标分子的部分。 人口数据可以涉及至少一种不同的生物性状和目标分子之间的至少一种关系。 也考虑了采用该方法预测的一个或多个表位。
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87.发明授权Systems and methods for determining bid value for content items to be placed on a rendered page 有权确定要放置在呈现页面上的内容项目的出价值的系统和方法公开(公告)号：US07689458B2
公开(公告)日：2010-03-30
申请号：US10977824
申请日：2004-10-29
申请人： David E. Heckerman , David M. Chickering , Christopher A. Meek , Brian Burdick , Li Li , Murali Vajjiravel , Ying Li , Rajeev Prasad , Raxit A. Kagalwala , Tarek Najm , Sachin Dhawan
发明人： David E. Heckerman , David M. Chickering , Christopher A. Meek , Brian Burdick , Li Li , Murali Vajjiravel , Ying Li , Rajeev Prasad , Raxit A. Kagalwala , Tarek Najm , Sachin Dhawan
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0241 , G06Q30/0247 , G06Q30/0256 , G06Q30/0601 , G06Q30/08 , G06Q40/04
摘要： Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.
摘要翻译： 用于确定由内容提供商针对在给定上下文呈现的页面（例如，网页）上的放置位置的出价的价值的系统和方法，例如在搜索引擎网络上发起的特定查询的搜索结果列表 现场提供。 此外，提供了系统和方法，用于基于出价值，在相对于页面上的其他内容项目的呈现页面上确定内容项目（例如广告和/或图像）的放置。
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公开(公告)号：US20080172351A1
公开(公告)日：2008-07-17
申请号：US11696595
申请日：2007-04-04
IPC分类号： G06N5/02
CPC分类号： G16B10/00
摘要： Computer-executable instructions for identifying associations are described herein. By way of example, a method for facilitating developing a treatment can include employing computer-executable instructions stored on one or more computer-readable media to determine correlations and utilizing at least some of the determined correlations to develop a treatment.
摘要翻译： 本文描述了用于识别关联的计算机可执行指令。 作为示例，用于促进开发治疗的方法可以包括采用存储在一个或多个计算机可读介质上的计算机可执行指令来确定相关性并利用所确定的至少一些相关性来开发治疗。
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89.发明授权公开(公告)号：US07370002B2
公开(公告)日：2008-05-06
申请号：US10163056
申请日：2002-06-05
申请人： David E. Heckerman , Martin Luo , Guy Shani , Mahbubul Alam Ali
发明人： David E. Heckerman , Martin Luo , Guy Shani , Mahbubul Alam Ali
IPC分类号： G06F17/60
CPC分类号： G06Q30/02 , G06Q30/0201 , G06Q30/0204 , G06Q30/0242 , G06Q30/0254 , G06Q30/0269 , G06Q30/0277
摘要： Advertisement response probabilities are utilized to alter advertisement scores. A plurality of possible advertisements is accessed from, for example, an advertisement database or advertisement pipeline. A response probability for each advertisement is determined. A response probability may be a probability that a user will “click,” or otherwise select an advertisement. Advertisements may be associated with probabilistic prediction models that take advertisement recipient attribute values as inputs and provide a probability distribution as output. A score associated with each of the possible advertisements is altered based on the response probability for each of the advertisements. Statistical prediction is used to determine how scores are to be altered. Advertisements with response probabilities less than a mean probability may have associated scores decreased. Conversely, advertisements with response probabilities greater than a mean probability may have associated scores increased.
摘要翻译： 广告响应概率用于改变广告评分。 可以从例如广告数据库或广告流水线访问多个可能的广告。 确定每个广告的响应概率。 响应概率可能是用户“点击”或以其他方式选择广告的概率。 广告可能与将广告收件人属性值作为输入并提供概率分布作为输出的概率预测模型相关联。 基于每个广告的响应概率来改变与每个可能的广告相关联的评分。 统计预测用于确定评分如何改变。 响应概率小于平均概率的广告可能会降低相关分数。 相反，具有大于平均概率的响应概率的广告可以具有相关联的分数增加。
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公开(公告)号：US07225200B2
公开(公告)日：2007-05-29
申请号：US10824108
申请日：2004-04-14
申请人： David M. Chickering , Bo Thiesson , Carl M. Kadie , David E. Heckerman , Christopher A. Meek , Allan Folting , Eric B. Vigesaa
发明人： David M. Chickering , Bo Thiesson , Carl M. Kadie , David E. Heckerman , Christopher A. Meek , Allan Folting , Eric B. Vigesaa
CPC分类号： G06F17/30592 , G06F2216/03 , Y10S707/99936 , Y10S707/99943
摘要： The present invention leverages machine learning techniques to provide automatic generation of conditioning variables for constructing a data perspective for a given target variable. The present invention determines and analyzes the best target variable predictors for a given target variable, employing them to facilitate the conveying of information about the target variable to a user. It automatically discretizes continuous and discrete variables utilized as target variable predictors to establish their granularity. In other instances of the present invention, a complexity and/or utility parameter can be specified to facilitate generation of the data perspective via analyzing a best target variable predictor versus the complexity of the conditioning variable(s) and/or utility. The present invention can also adjust the conditioning variables (i.e., target variable predictors) of the data perspective to provide an optimum view and/or accept control inputs from a user to guide/control the generation of the data perspective.
摘要翻译： 本发明利用机器学习技术来提供用于为给定目标变量构建数据透视图的自动生成调节变量。 本发明确定和分析给定目标变量的最佳目标变量预测变量，使用它们来促进向用户传达关于目标变量的信息。 它自动离散化用作目标变量预测变量的连续和离散变量以确定其粒度。 在本发明的其他实例中，可以规定复杂性和/或效用参数，以通过分析最佳目标变量预测器与调节变量和/或效用的复杂性来促进数据透视的产生。 本发明还可以调整数据透视图的调节变量（即，目标变量预测器），以提供最佳视图和/或接受来自用户的控制输入以指导/控制数据视角的产生。
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