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公开(公告)号：US20120158630A1
公开(公告)日：2012-06-21
申请号：US12971191
申请日：2010-12-17
申请人： Tauhid Rashed Zaman , Jurgen Anne Francois Marie Van Gael , David Stern , Ralf Herbrich , Gilad Lotan
发明人： Tauhid Rashed Zaman , Jurgen Anne Francois Marie Van Gael , David Stern , Ralf Herbrich , Gilad Lotan
IPC分类号： G06N3/00 , G06F15/173
CPC分类号： G06N5/04 , G06N99/005 , G06Q30/02 , H04L43/08 , H04L51/32
摘要： One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
摘要翻译： 公开了一种或多种技术和/或系统来预测消息在社交网络上的传播。 训练一个预测模型，以确定使用正和负信息传播反馈在社交网络上传播信息的概率，可以在信息传播的期望时间段内监视社交网络时收集信息。 可以将特定消息输入到预测模型，预测模型可以确定消息在社交网络上的传播概率，例如多少连接可以接收消息的至少一部分和/或至少一部分的可能性 的消息到达社交网络中的各个连接。
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公开(公告)号：US08473437B2
公开(公告)日：2013-06-25
申请号：US12971191
申请日：2010-12-17
申请人： Tauhid Rashed Zaman , Jurgen Anne Francois Marie Van Gael , David Stern , Ralf Herbrich , Gilad Lotan
发明人： Tauhid Rashed Zaman , Jurgen Anne Francois Marie Van Gael , David Stern , Ralf Herbrich , Gilad Lotan
CPC分类号： G06N5/04 , G06N99/005 , G06Q30/02 , H04L43/08 , H04L51/32
摘要： One or more techniques and/or systems are disclosed for predicting propagation of a message on a social network. A predictive model is trained to determine a probability of propagation of information on the social network using both positive and negative information propagation feedback, which may be collected while monitoring the social network over a desired period of time for information propagation. A particular message can be input to the predictive model, which can determine a probability of propagation of the message on the social network, such as how many connections may receive at least a portion of the message and/or a likelihood of at least a portion of the message reaching respective connections in the social network.
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公开(公告)号：US08589317B2
公开(公告)日：2013-11-19
申请号：US12970158
申请日：2010-12-16
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06N3/08
摘要： Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set.
摘要翻译： 许多计算场景包括对一个或多个类别内的内容项进行分类。 内容项集合可能太大以致人类不能进行分类，但是自动分类器（例如，人造神经网络）可能不能够以可接受的准确度对所有内容项进行分类。 相反，自动分类器可以在分类各个内容项目时计算分类置信度。 具有低分类置信度的内容项目可以被发送到人类分类器，并且可以与人类分类器识别的类别一起被添加到训练集合中。 然后可以使用训练集再次训练自动分类器，从而逐渐改进自动分类器的分类置信度，同时节省人类分类器的参与。 此外，可以奖励人类分类器对内容项进行分类，并且可以在选择训练集的内容项时考虑这种奖励的成本。
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公开(公告)号：US08706653B2
公开(公告)日：2014-04-22
申请号：US12963352
申请日：2010-12-08
申请人： Gjergji Kasneci , Jurgen Anne Francois Marie Van Gael , Thore Kraepel , Ralf Herbrich , David Stern
发明人： Gjergji Kasneci , Jurgen Anne Francois Marie Van Gael , Thore Kraepel , Ralf Herbrich , David Stern
IPC分类号： G06F15/18
CPC分类号： G06N7/005
摘要： Knowledge corroboration is described. In an embodiment many judges provide answers to many questions so that at least one answer is provided to each question and at least some of the questions have answers from more than one judge. In an example a probabilistic learning system takes features describing the judges or the questions or both and uses those features to learn an expertise of each judge. For example, the probabilistic learning system has a graphical assessment component which aggregates the answers in a manner which takes into account the learnt expertise in order to determine enhanced answers. In an example the enhanced answers are used for knowledge base clean-up or web-page classification and the learnt expertise is used to select judges for future questions. In an example the probabilistic learning system has a logical component that propagates answers according to logical relations between the questions.
摘要翻译： 描述知识佐证。 在一个实施例中，许多法官为许多问题提供答案，使得至少一个答案被提供给每个问题，并且至少一些问题具有来自多于一个法官的答案。 在一个例子中，概率学习系统采用描述法官或问题或两者的特征，并使用这些特征来学习每个法官的专业知识。 例如，概率学习系统具有图形评估组件，其以考虑到所学习的专业知识的方式聚集答案，以便确定增强的答案。 在一个例子中，增强的答案用于知识库清理或网页分类，并且学习的专业知识用于为将来的问题选择法官。 在一个例子中，概率学习系统具有根据问题之间的逻辑关系传播答案的逻辑组件。
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公开(公告)号：US20080004096A1
公开(公告)日：2008-01-03
申请号：US11421913
申请日：2006-06-02
申请人： Thore K. H. Graepel , Ralf Herbrich , David Stern
发明人： Thore K. H. Graepel , Ralf Herbrich , David Stern
IPC分类号： A63F9/24
摘要： We describe an apparatus for learning to predict moves in games such as chess, Go and the like, from historical game records. We obtain a probability distribution over legal moves in a given board configuration. This enables us to provide an automated game playing system, a training tool for players and a move selector/sorter for input to a game tree search system. We use a pattern extraction system to select patterns from historical game records. Our learning algorithm learns a distribution over the values of a move given a board position based on local pattern context. In another embodiment we use an Independent Bernoulli model whereby we assume each moved is played independently of other available moves.
摘要翻译： 我们描述一种从历史游戏记录中学习来预测诸如象棋，Go等游戏中的移动的装置。 在给定的电路板配置中，我们获得了合法移动的概率分布。 这使我们能够提供一种自动游戏系统，用于玩家的训练工具和用于输入到游戏树搜索系统的移动选择器/分选器。 我们使用模式提取系统从历史游戏记录中选择模式。 我们的学习算法基于局部模式上下文学习一个给定一个董事会职位的动作值的分布。 在另一个实施例中，我们使用独立的伯努利模型，由此我们假设每个移动都是独立于其他可用移动进行的。
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6.发明授权Recommending items to users utilizing a bi-linear collaborative filtering model 有权使用双线性协同过滤模型向用户推荐项目公开(公告)号：US08781915B2
公开(公告)日：2014-07-15
申请号：US12253854
申请日：2008-10-17
申请人： Ralf Herbrich , Thore Graepel , David Stern
发明人： Ralf Herbrich , Thore Graepel , David Stern
IPC分类号： G06Q10/00
CPC分类号： G06Q30/0633 , G06Q30/02 , G06Q30/0203
摘要： A recommender system may be used to predict a user behavior that a user will give in relation to an item. In an embodiment such predictions are used to enable items to be recommended to users. For example, products may be recommended to customers, potential friends may be recommended to users of a social networking tool, organizations may be recommended to automated users or other items may be recommended to users. In an embodiment a memory stores a data structure specifying a bi-linear collaborative filtering model of user behaviors. In the embodiment an automated inference process may be applied to the data structure in order to predict a user behavior given information about a user and information about an item. For example, the user information comprises user features as well as a unique user identifier.
摘要翻译： 推荐系统可以用于预测用户将相对于项目给出的用户行为。 在一个实施例中，这样的预测用于使得可以向用户推荐项目。 例如，产品可能会推荐给客户，潜在的朋友可能会推荐给社交网络工具的用户，组织可能会推荐给自动化用户或其他项目可能推荐给用户。 在一个实施例中，存储器存储指定用户行为的双线性协同过滤模型的数据结构。 在该实施例中，自动推理过程可以应用于数据结构，以便预测给定关于用户的信息的用户行为和关于项目的信息。 例如，用户信息包括用户特征以及唯一的用户标识符。
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公开(公告)号：US20080027570A1
公开(公告)日：2008-01-31
申请号：US11532452
申请日：2006-09-15
申请人： Thore K.H. Graepel , Ralf Herbrich , David Stern
发明人： Thore K.H. Graepel , Ralf Herbrich , David Stern
IPC分类号： G06F19/00
摘要： Scoring a board configuration for a territory board game is often not straightforward and yet there is a desire to determine such scores quickly and accurately. For example, in the game of GO, determining the score at the end of the game involves assessing whether stones on the board are alive or dead which is a difficult judgment. Given a board configuration, the game is played by a scoring system to obtain a terminal board configuration. This is repeated to obtain a plurality of terminal board configurations from which an assessment can be made as to how likely each board position is to be won by a particular player at the end of the game. The scoring system obtains the terminal board configurations by playing random moves or by making a biased sampling of moves. The biased sampling is made using an evaluation function or in any suitable way. In the game of GO, seki positions are quickly and easily identified. An automated game playing system uses the output of the scoring system to assess when to offer to end a game. The output of the scoring system can also be used to provide hints to players during a game.
摘要翻译： 为领土板游戏评分板块配置通常并不简单，但是希望快速，准确地确定这些分数。 例如，在GO游戏中，确定游戏结束时的分数包括评估板上的石头是活着还是死亡，这是一个困难的判断。 给定一个电路板配置，该游戏由得分系统进行，以获得一个终端板配置。 这是重复的，以获得多个终端板配置，从该终端板配置可以评估在游戏结束时由特定玩家赢得每个板位置的可能性。 评分系统通过播放随机动作或通过偏移采样来获得终端板配置。 使用评估函数或以任何合适的方式进行偏置采样。 在GO的游戏中，seki的位置很容易被识别。 自动游戏系统使用评分系统的输出来评估何时提供结束游戏。 评分系统的输出也可用于在游戏过程中向玩家提供提示。
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公开(公告)号：US20120158620A1
公开(公告)日：2012-06-21
申请号：US12970158
申请日：2010-12-16
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06N3/08
摘要： Many computing scenarios involve the classification of content items within one or more categories. The content item set may be too large for humans to classify, but an automated classifier (e.g., an artificial neural network) may not be able to classify all content items with acceptable accuracy. Instead, the automated classifier may calculate a classification confidence while classifying respective content items. Content items having a low classification confidence may be sent to a human classifier, and may be added, along with the categories identified by the human classifier, to a training set. The automated classifier may then be retrained using the training set, thereby incrementally improving the classification confidence of the automated classifier while conserving the involvement of human classifiers. Additionally, human classifiers may be rewarded for classifying the content items, and the costs of such rewards may be considered while selecting content items for the training set.
摘要翻译： 许多计算场景包括对一个或多个类别内的内容项进行分类。 内容项集合可能太大以致人类不能进行分类，但是自动分类器（例如，人造神经网络）可能不能够以可接受的准确度对所有内容项进行分类。 相反，自动分类器可以在分类各个内容项目时计算分类置信度。 具有低分类置信度的内容项目可以被发送到人类分类器，并且可以与人类分类器识别的类别一起被添加到训练集合中。 然后可以使用训练集再次训练自动分类器，从而逐渐改进自动分类器的分类置信度，同时节省人类分类器的参与。 此外，可以奖励人类分类器对内容项进行分类，并且可以在选择训练集的内容项时考虑这种奖励的成本。
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公开(公告)号：US08645298B2
公开(公告)日：2014-02-04
申请号：US12912428
申请日：2010-10-26
申请人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
发明人： Philipp Hennig , David Stern , Thore Graepel , Ralf Herbrich
CPC分类号： G06N99/005 , G06N7/005
摘要： Machine learning techniques may be used to train computing devices to understand a variety of documents (e.g., text files, web pages, articles, spreadsheets, etc.). Machine learning techniques may be used to address the issue that computing devices may lack the human intellect used to understand such documents, such as their semantic meaning. Accordingly, a topic model may be trained by sequentially processing documents and/or their features (e.g., document author, geographical location of author, creation date, social network information of author, and/or document metadata). Additionally, as provided herein, the topic model may be used to predict probabilities that words, features, documents, and/or document corpora, for example, are indicative of particular topics.
摘要翻译： 机器学习技术可用于训练计算设备以理解各种文档（例如，文本文件，网页，文章，电子表格等）。 可以使用机器学习技术来解决计算设备可能缺乏用于理解这样的文档的人类智力的问题，例如其语义意义。 因此，主题模型可以通过顺序处理文档和/或其特征（例如，文档作者，作者的地理位置，创作日期，作者的社交网络信息和/或文档元数据）来进行培训。 另外，如本文所提供的，主题模型可以用于预测词，特征，文档和/或文档语料库例如表示特定主题的概率。
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公开(公告)号：US08433660B2
公开(公告)日：2013-04-30
申请号：US12628421
申请日：2009-12-01
摘要： Managing a portfolio of experts is described where the experts may be for example, automated experts or human experts. In an embodiment a selection engine selects an expert from a portfolio of experts and assigns the expert to a specified task. For example, the selection engine has a Bayesian machine learning system which is iteratively updated each time an experts performance on a task is observed. For example, sparsely active binary task and expert feature vectors are input to the selection engine which maps those feature vectors to a multi-dimensional trait space using a mapping learnt by the machine learning system. In examples, an inner product of the mapped vectors gives an estimate of a probability distribution over expert performance. In an embodiment the experts are automated problem solvers and the task is a hard combinatorial problem such as a constraint satisfaction problem or combinatorial auction.
摘要翻译： 描述专家组合的描述，专家可能是例如，自动化专家或人类专家。 在一个实施例中，选择引擎从专家组合中选择专家，并将专家分配给指定的任务。 例如，选择引擎具有贝叶斯机器学习系统，每当观察到任务上的专家表现时，该学习系统被迭代地更新。 例如，将稀疏活动的二进制任务和专家特征向量输入到使用机器学习系统学习的映射将这些特征向量映射到多维特征空间的选择引擎。 在示例中，映射向量的内积给出了对专家性能的概率分布的估计。 在一个实施例中，专家是自动化问题解决者，并且任务是诸如约束满足问题或组合拍卖之类的硬组合问题。
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