-
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。
-
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。
-
公开(公告)号：US20110066577A1
公开(公告)日：2011-03-17
申请号：US12559921
申请日：2009-09-15
CPC分类号： G06N99/005
摘要： Machine learning using relational databases is described. In an embodiment a model of a probabilistic relational database is formed by augmenting relation schemas of a relational database with probabilistic attributes. In an example, the model comprises constraints introduced by linking the probabilistic attributes using factor statements. For example, a compiler translates the model into a factor graph data structure which may be passed to an inference engine to carry out machine learning. For example, this enables machine learning to be integrated with the data and it is not necessary to pre-process or reformat large scale data sets for a particular problem domain. In an embodiment a machine learning system for estimating skills of players in an online gaming environment is provided. In another example, a machine learning system for data mining of medical data is provided. In some examples, missing attribute values are filled using machine learning results.
摘要翻译： 描述使用关系数据库的机器学习。 在一个实施例中，通过用概率属性来增加关系数据库的关系模式来形成概率关系数据库的模型。 在一个例子中，模型包括通过使用因子语句链接概率属性引入的约束。 例如，编译器将该模型转换为因子图数据结构，该结构可被传递给推理机以执行机器学习。 例如，这使得机器学习能够与数据集成，并且不需要为特定问题域预处理或重新格式化大规模数据集。 在一个实施例中，提供了一种用于估计在线游戏环境中的玩家的技能的机器学习系统。 在另一示例中，提供了用于医疗数据的数据挖掘的机器学习系统。 在一些示例中，使用机器学习结果填充缺少的属性值。
-
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.
-
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。
-
-
-
-
搜索结果
5 条记录 (耗时 0.014 秒)
