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    • 51. 发明授权
    • HMM learning device and method, program, and recording medium
    • HMM学习装置和方法,程序和记录介质
    • US08725510B2
    • 2014-05-13
    • US12829984
    • 2010-07-02
    • Yukiko YoshiikeKenta KawamotoKuniaki NodaKohtaro Sabe
    • Yukiko YoshiikeKenta KawamotoKuniaki NodaKohtaro Sabe
    • G10L15/14
    • G06N99/005
    • An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
    • HMM(隐马尔可夫模型)学习装置包括:用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元,基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息 观察信号; 以及存储单元,用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习,并且基于所述频率变量来估计所述状态转换概率和观察概率。
    • 52. 发明申请
    • HMM LEARNING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM
    • HMM学习设备和方法,程序和记录介质
    • US20110010176A1
    • 2011-01-13
    • US12829984
    • 2010-07-02
    • Yukiko YOSHIIKEKenta KawamotoKuniaki NodaKohtaro Sabe
    • Yukiko YOSHIIKEKenta KawamotoKuniaki NodaKohtaro Sabe
    • G10L15/14
    • G06N99/005
    • An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
    • HMM(隐马尔可夫模型)学习装置包括:用于学习作为代理可以执行的动作的函数的状态转移概率的学习单元,基于代理已经执行的动作执行的HMM的学习以及作出的时间序列信息 观察信号; 以及存储单元,用于将所述学习单元的学习结果存储为包括状态转换概率表和观察概率表的内部模型数据; 学习单元计算用于HMM状态转换和HMM观察概率的估计计算的频率变量; 存储单元保持状态转移概率表中分别对应于状态转换概率和每个观察概率的频率变量; 并且所述学习单元使用由所述存储单元保持的频率变量来执行学习,并且基于所述频率变量来估计所述状态转换概率和观察概率。
    • 53. 发明申请
    • INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM
    • 信息处理设备,信息处理方法和程序
    • US20100318478A1
    • 2010-12-16
    • US12791240
    • 2010-06-01
    • Yukiko YoshiikeKenta KawamotoKuniaki NodaKohtaro Sabe
    • Yukiko YoshiikeKenta KawamotoKuniaki NodaKohtaro Sabe
    • G06N5/02G06F15/18
    • G06N3/006G06K9/00335G06K9/00664G06K9/6297
    • An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy.
    • 一种信息处理设备,包括:计算单元,被配置为基于通过执行状态转移概率的学习获得的状态转移概率模型来计算作为达到当前状态的动作的代理的状态序列的当前状态序列候选 由状态转移概率规定的模式,其状态将根据由能够执行动作的代理执行的动作而转变,并且使用由代理执行的动作从该状态观察到预定观察值的观察概率, 以及当代理人执行动作时在代理处观察到的观察值; 以及确定单元,被配置为根据预定策略来确定所述代理使用所述当前状态序列候选来执行的动作。
    • 54. 发明申请
    • Data processing device, data processing method, and program
    • 数据处理装置,数据处理方法和程序
    • US20070280006A1
    • 2007-12-06
    • US11732644
    • 2007-04-04
    • Kazumi AoyamaKohtaro SabeHideki Shimomura
    • Kazumi AoyamaKohtaro SabeHideki Shimomura
    • G11C7/10
    • G06F17/30551G06K9/0057G06K9/6251
    • A data processing device for processing time-sequence data includes a data extracting unit operable to extract time-sequence data for a predetermined time unit from time-sequence data; and a processing unit operable to obtain scores for nodes of an SOM configured from multiple nodes provided with a spatial array configuration, the scores showing applicability to time-sequence data for a predetermined time unit thereof, wherein the node with the best score thereof is determined to be the winning node which is the node most applicable; wherein the processing unit obtains scores as to the time-sequence data for one predetermined time unit, regarding a distance-restricted node wherein distance from the winning node as to the time-sequence for a predetermined time unit immediately preceding the time-sequence data of one predetermined time unit is within a predetermined distance; and wherein the distance-restricted node with the best the score is determined to be the winning node thereof.
    • 一种用于处理时间序列数据的数据处理装置,包括数据提取单元,用于从时间序列数据中提取预定时间单位的时间序列数据; 以及处理单元,其可操作以获得由具有空间阵列配置的多个节点配置的SOM的节点的分数,所述分数显示对于其预定时间单位的时间序列数据的适用性,其中确定具有最佳分数的节点 作为最适用节点的获胜节点; 其中处理单元获得关于一个预定时间单位的时间序列数据的分数,关于距离受限节点的距离限制节点,其中距离获胜节点的距离与时间序列的时间顺序紧邻在时间序列数据之前的预定时间单位 一个预定时间单位在预定距离内; 并且其中具有最佳分数的距离限制节点被确定为其获胜节点。
    • 55. 发明申请
    • INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM
    • 信息处理设备,信息处理方法和程序
    • US20110305384A1
    • 2011-12-15
    • US13097288
    • 2011-04-29
    • Kazumi AOYAMAKohtaro Sabe
    • Kazumi AOYAMAKohtaro Sabe
    • G06K9/62
    • G06K9/00281G10L15/25G10L25/78
    • An information processing apparatus includes a first generation unit that generates learning images corresponding to a learning moving image, a first synthesis unit that generates a synthesized learning image such that a plurality of the learning images is arranged at a predetermined location and synthesized, a learning unit that computes a feature amount of the generated synthesized learning image, and performs statistical learning using the feature amount to generate a classifier, a second generation unit that generates determination images, a second synthesis unit that generates a synthesized determination image such that a plurality of the determination images is arranged at a predetermined location and synthesized, a feature amount computation unit that computes a feature amount of the generated synthesized determination image, and a determination unit that determines whether or not the determination image corresponds to a predetermined movement.
    • 一种信息处理设备,包括:产生与学习运动图像相对应的学习图像的第一生成单元;生成合成学习图像的第一合成单元,使得多个学习图像被布置在预定位置并合成;第一合成单元, 其计算所生成的合成学习图像的特征量,并且使用特征量进行统计学习以生成分类器,产生确定图像的第二生成单元,生成合成确定图像的第二合成单元, 确定图像被布置在预定位置并被合成,计算所生成的合成确定图像的特征量的特征量计算单元和确定判定图像是否对应于预定移动的确定单元。
    • 56. 发明授权
    • Identifying temporal sequences using a recurrent self organizing map
    • 使用循环自组织图识别时间序列
    • US07725412B2
    • 2010-05-25
    • US11732644
    • 2007-04-04
    • Kazumi AoyamaKohtaro SabeHideki Shimomura
    • Kazumi AoyamaKohtaro SabeHideki Shimomura
    • G06F15/00
    • G06F17/30551G06K9/0057G06K9/6251
    • A data processing device for processing time-sequence data includes a data extracting unit extracting time-sequence data for a predetermined time unit from time-sequence data; and a processing unit obtaining scores for nodes of an SOM configured from multiple nodes provided with a spatial array configuration, the scores showing applicability to time-sequence data for a predetermined time unit thereof. The node with the best score is determined to be the winning node which is the node most applicable. The processing unit obtains scores as to the time-sequence data for one predetermined time unit, regarding a distance-restricted node wherein distance from the winning node as to the time-sequence for a predetermined time unit immediately preceding the time-sequence data of one predetermined time unit is within a predetermined distance. The distance-restricted node with the best the score is determined to be the winning node.
    • 一种用于处理时间序列数据的数据处理装置,包括数据提取单元,从时间序列数据提取预定时间单位的时间序列数据; 以及处理单元,其获得由具有空间阵列配置的多个节点配置的SOM的节点,所述分数显示对于其预定时间单位的时间序列数据的适用性。 具有最佳分数的节点被确定为作为最适用节点的获胜节点。 处理单元获得关于一个预定时间单位的时间序列数据的分数,关于距离限制节点的距离限制节点,其中距离胜利节点的距离与紧接在一个的时间序列数据之前的预定时间单位的时间序列 预定时间单位在预定距离内。 得分最高的距离限制节点被确定为获胜节点。
    • 58. 发明授权
    • Image processing system, learning device and method, and program
    • 图像处理系统,学习装置和方法,程序
    • US08582887B2
    • 2013-11-12
    • US11813404
    • 2005-12-26
    • Hirotaka SuzukiAkira NakamuraTakayuki YoshigaharaKohtaro SabeMasahiro Fujita
    • Hirotaka SuzukiAkira NakamuraTakayuki YoshigaharaKohtaro SabeMasahiro Fujita
    • G06K9/00
    • G06K9/00288G06K9/6211G06K9/623G06T7/00
    • The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section 23. Similarly, feature points are extracted from a learning-use input image containing a model object contained in the learning-use model image, feature amounts are extracted based on these feature points, and these feature amounts are compared with the feature amounts registered in a learning-use model registration section 23. A feature amount that has formed a pair the greatest number of times as a result of the comparison is registered in the model dictionary registration section 12 as the feature amount to be used in the recognition process. The present invention is applicable to a robot.
    • 本发明涉及图像处理系统,学习装置和方法以及能够容易地提取在识别处理中使用的特征量的程序。 从学习用模型图像提取特征点,基于特征点提取特征量,并且将特征量登记在学习用模型字典注册部23中。同样,从学习用途中提取特征点 基于这些特征点提取含有包含在学习用模型图像中的模型对象的输入图像,并将这些特征量与在学习用模型登记部23中登记的特征量进行比较。特征量 作为比较的结果,在模型字典登记部12中登记了作为识别处理中使用的特征量的最大次数的对。 本发明可应用于机器人。