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81. 发明申请

US20030191726A1 Machine decisions based on preferential voting techniques 有权
标题翻译：基于优惠投票技术的机器决策
公开(公告)号：US20030191726A1
公开(公告)日：2003-10-09
申请号：US10116835
申请日：2002-04-05
发明人： Evan R. Kirshenbaum
IPC分类号： G06N005/02 , G06N005/00 , G06G007/00 , G06E003/00 , G06E001/00 , G06F015/18 , G06F017/00
CPC分类号： G06K9/6292 , G06N3/086
摘要： A method and apparatus for computing an overall or aggregate decision based on intermediate decisions as to which of a set of alternatives best characterize an object. The alternatives are partitioned into at least two series of preferences corresponding to at least two intermediate rankings. Various embodiments may base the intermediate rankings on: a machine learning technique; a decision tree; a belief network; a neural network; a static model; a program; or an evolutionary training method. Based on the preferences, a weak alternative is selected and removed from the series. The selection of the weak alternative may include identifying which alternatives lose pairwise to the other alternatives, are excluded from the first preferences, are included in the last preferences, or have a lowest average preference ranking. The selecting and removing continue until the remaining alternatives are the aggregate decision. Various embodiments may be applied to classification problems, prediction problems or selection problems.
摘要翻译：一种用于基于关于一组替代物中的哪一种最佳地表征对象的中间决策来计算总体或聚合决策的方法和装置。备选方案被划分为对应于至少两个中间排名的至少两个系列的偏好。各种实施例可以基于以下机器学习技术的中间排名：决策树信仰网络; 神经网络静态模型; 一个程序或进化训练方法。根据偏好，从系列中选出并删除了弱选项。弱选择的选择可以包括确定哪些替代物与其他替代方案丢失成对，从第一偏好中排除，包括在最后的偏好中，或者具有最低的平均偏好排名。选择和删除继续，直到剩余的替代方案是总决定。各种实施例可以应用于分类问题，预测问题或选择问题。

82. 发明申请

US20030154175A1 Back-propagation neural network with enhanced neuron characteristics 有权
标题翻译：具有增强的神经元特征的反向传播神经网络
公开(公告)号：US20030154175A1
公开(公告)日：2003-08-14
申请号：US10073224
申请日：2002-02-13
发明人： Bingxue Shi , Chun Lu , Lu Chen
IPC分类号： G06N003/067 , G06F015/18 , G06G007/00 , G06N003/02 , G06E003/00 , G06E001/00 , G06N003/063 , G06N003/06
CPC分类号： G06N3/063 , G06N3/0481
摘要： A neural network system includes a feedforward network comprising at least one neuron circuit for producing an activation function and a first derivative of the activation function and a weight updating circuit for producing updated weights to the feedforward network. The system also includes an error back-propagation network for receiving the first derivative of the activation function and to provide weight change data information to the weight updating circuit.
摘要翻译：神经网络系统包括前馈网络，其包括用于产生激活功能的至少一个神经元电路和激活功能的一阶导数，以及用于向前馈网络产生更新权重的权重更新电路。该系统还包括用于接收激活功能的一阶导数的误差反向传播网络，并向权重更新电路提供权重改变数据信息。

83. 发明申请

US20030140020A1 Plausible neural network with supervised and unsupervised cluster analysis 有权
标题翻译：具有监督和无监督聚类分析的合理神经网络
公开(公告)号：US20030140020A1
公开(公告)日：2003-07-24
申请号：US10294773
申请日：2002-11-15
发明人： Yuan Yan Chen , Joseph Chen
IPC分类号： G06E001/00 , G06E003/00 , G06N003/02 , G06G007/00 , G06F015/18
CPC分类号： G06N3/08 , G06N3/0436
摘要： A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.
摘要翻译：一个合理的神经网络（PLANN）是一种人工神经网络，具有由相互信息给出的权重连接，具有推理和学习的能力，但保留了生物神经网络的许多特征。学习算法是基于统计估计，其比当前使用的梯度方法更快。训练后的网络成为模糊/信念网络; 推理和权重是可交换的，因此知识提取变得简单。 PLANN在单一网络架构中执行关联记忆，监督，半监督，无监督学习和功能/关系近似。该网络架构可以通过模拟VLSI电路设计轻松实现。

84. 发明申请

US20030137707A1 Optical system for certain mathematical operations 失效
标题翻译：用于某些数学运算的光学系统
公开(公告)号：US20030137707A1
公开(公告)日：2003-07-24
申请号：US10051732
申请日：2002-01-22
发明人： John L. Johnson
IPC分类号： G06E003/00
CPC分类号： G06E3/001
摘要： An optical system utilizing phosphors to perform mathematical operations without the direct or necessary use of an electronic component or electrical power source is disclosed. The luminenscent and quenching properties of phosphors are combined with at least one first-order relaxation subsystem such that when the optical system achieves equilibrium, it will have performed certain mathematical operations. The precise mathematical operation to be performed is determined by controlling the materials utilized, light inputs, and certain variables within the optical system.
摘要翻译：公开了一种使用磷光体进行数学运算而不需要直接或必要使用电子部件或电源的光学系统。荧光体的光亮和淬灭特性与至少一个一阶松弛子系统组合，使得当光学系统达到平衡时，它将执行某些数学运算。要执行的精确数学运算是通过控制光学系统中使用的材料，光输入和某些变量来确定的。

85. 发明申请

US20030065631A1 Pipelined hardware implementation of a neural network circuit 失效
标题翻译：流水线硬件实现神经网络电路
公开(公告)号：US20030065631A1
公开(公告)日：2003-04-03
申请号：US09970002
申请日：2001-10-03
发明人： Chad B. McBride
IPC分类号： G06E001/00 , G06E003/00 , G06N003/02 , G06G007/00 , G06F015/18 , G06N003/06 , G06N003/063 , G06N003/067
CPC分类号： G06N3/063
摘要： In a first aspect, a pipelined hardware implementation of a neural network circuit includes an input stage, two or more processing stages and an output stage. Each processing stage includes one or more processing units. Each processing unit includes storage for weighted values, a plurality of multipliers for multiplying input values by weighted values, an adder for adding products outputted from product multipliers, a function circuit for applying a non-linear function to the sum outputted by the adder, and a register for storing the output of the function circuit.
摘要翻译：在第一方面，神经网络电路的流水线硬件实现包括输入级，两个或多个处理级和输出级。每个处理阶段包括一个或多个处理单元。每个处理单元包括用于加权值的存储，用于将输入值乘以加权值的多个乘法器，用于将从乘积乘法器输出的乘积相加的加法器，用于对加法器输出的和应用非线性函数的函数电路，以及用于存储功能电路的输出的寄存器。

86. 发明申请

US20030055797A1 Neural network system, software and method of learning new patterns without storing existing learned patterns 审中-公开
标题翻译：神经网络系统，软件和学习新模式的方法，而不存储现有的学习模式
公开(公告)号：US20030055797A1
公开(公告)日：2003-03-20
申请号：US10196855
申请日：2002-07-16
发明人： Seiji Ishihara
IPC分类号： G06E001/00 , G06E003/00 , G06N003/02 , G06G007/00 , G06F015/18
CPC分类号： G06K9/6273 , G06N3/08
摘要： Learning using a neural network is improved for a classification problem by recollecting input patterns from the learned data without storing the original input data patterns. The neural network includes input elements in an input layer, middle elements in a middle layer and output elements in an output layer. The elements between two layers are related with each other by a corresponding weight. An output function of the middle and output layers includes a radial basis function (RBF). The recollected input patterns are generated based upon two parameters including a first vector indicating a central position o the RBF and a second vector indicating a range and a direction of the RBF. The recollected input patterns are used to improve additional learning of a new set of input patterns.
摘要翻译：通过从学习的数据中回收输入模式而不存储原始输入数据模式，通过使用神经网络的学习被改进用于分类问题。神经网络包括输入层中的输入元素，中间层中的中间元素和输出层中的输出元素。两层之间的元素通过相应的重量彼此相关。中间和输出层的输出函数包括径向基函数（RBF）。基于包括指示RBF的中心位置的第一向量和指示RBF的范围和方向的第二向量的两个参数来生成回收的输入模式。回收的输入模式用于改进新一组输入模式的附加学习。

87. 发明申请

US20030028705A1 Computer device based on associative memory 审中-公开
标题翻译：基于联想记忆的计算机设备
公开(公告)号：US20030028705A1
公开(公告)日：2003-02-06
申请号：US10086544
申请日：2002-03-04
发明人： Yutaka Arima
IPC分类号： G11C005/00 , G06F012/00 , G06E001/00 , G06E003/00 , G06N003/02 , G06G007/00 , G06F015/18 , G06F012/14 , G06F012/16 , G06F013/00 , G06F013/28
CPC分类号： G06N7/08 , G06N3/02 , G11C15/00
摘要： The associative memory-based computer includes at least one associative memory, a plurality of associative data memories capable of temporarily holding input or output data of the associative memory, and a value judgement device receiving part of the data held in the associative data memory. The associative memory is formed of a chaotic neural network. The associative data memories include a first associative data memory sending/receiving data directly to/from the associative memory, and a plurality of second associative data memories sending/receiving data to/from the associative memory via the first associative data memory.
摘要翻译：所述基于联想存储器的计算机包括至少一个关联存储器，能临时保存所述关联存储器的输入或输出数据的多个关联数据存储器，以及接收保持在所述关联数据存储器中的数据的一部分的值判断装置。联想记忆由混沌神经网络组成。关联数据存储器包括：第一关联数据存储器，直接向/从关联存储器发送/接收数据;以及多个第二关联数据存储器，经由第一关联数据存储器向/从联想存储器发送/接收数据。

88. 发明申请

US20030018595A1 Machine learning method 失效
标题翻译：机器学习方法
公开(公告)号：US20030018595A1
公开(公告)日：2003-01-23
申请号：US09882502
申请日：2001-06-15
发明人： Hung-Han Chen , Lawrence Hunter , Harry Towsley Poteat , Kristin Kendall Snow
IPC分类号： G06F015/18 , G06E001/00 , G06E003/00 , G06G007/00 , G06N005/00 , G06F017/00
CPC分类号： G06K9/6228 , G06N99/005
摘要： A method for using machine learning to solve problems having either a nullpositivenull result (the event occurred) or a nullnegativenull result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.
摘要翻译：一种使用机器学习来解决具有“正”结果（事件发生）或“否定”结果（不发生事件）的问题的方法，其中肯定结果的概率非常低，并且后果积极的结果是显着的。获得培训数据，并将该数据的一部分进行蒸馏以应用于机器学习系统。训练数据包括与正结果相对应的一些记录，来自对应于否定结果的记录中的一些最近邻居，以及与否定结果相对应的一些其他记录。机器学习系统使用协同进化方法来获得用于在多个周期之后预测结果的规则集。机器系统使用导出用于问题类型的适应度函数，例如基于规则的灵敏度和阳性预测值的适应度函数。规则使用整套训练数据进行验证。

89. 发明申请

US20020147694A1 Retraining trainable data classifiers 审中-公开
标题翻译：再培训可数据分类器
公开(公告)号：US20020147694A1
公开(公告)日：2002-10-10
申请号：US09773116
申请日：2001-01-31
发明人： Derek M. Dempsey , Kate Butchart , Phil W. Hobson
IPC分类号： G06G007/00 , G06E003/00 , G06E001/00 , G06F015/18
CPC分类号： G06K9/6256 , G06N3/08
摘要： A method and apparatus is provided for retraining a trainable data classifier (for example, a neural network). Data provided for retraining the classifier is compared with training data previously used to train the classifier, and a measure of the degree of conflict between the new and old training data is calculated. This measure is compared with a predetermined threshold to determine whether the new data should be used in retraining the data classifier. New training data which is found to conflict with earlier data may be further reviewed manually for inclusion.
摘要翻译：提供了一种用于再训练可训练数据分类器（例如，神经网络）的方法和装置。将为再分类器提供的数据与先前用于训练分类器的训练数据进行比较，并计算新旧训练数据之间的冲突程度。将该测量与预定阈值进行比较，以确定是否应该在再训练数据分类器中使用新数据。发现与早期数据相冲突的新的训练数据可能会被手动进一步检查以供列入。

90. 发明申请

US20020107841A1 Systems and methods for discovering partially periodic event patterns 有权
标题翻译：用于发现部分周期性事件模式的系统和方法
公开(公告)号：US20020107841A1
公开(公告)日：2002-08-08
申请号：US09739432
申请日：2000-12-18
发明人： Joseph L. Hellerstein , Sheng Ma
IPC分类号： G06F007/00 , G06F017/30 , G06E001/00 , G06E003/00 , G06G007/00 , G06F015/18
CPC分类号： G06K9/62 , G06F11/3452 , G06F2201/86 , G06F2201/88 , G06F2216/03 , Y10S707/99936
摘要： Systems and methods for discovering partially periodic temporal associations, referred to herein as p-patterns, are provided. For example, a p-pattern in computer networks might comprise five repetitions every 30 seconds of a port-down event followed by a port-up event, which in turn is followed by a random gap until the next five repetitions of these events. In one embodiment, the present invention comprises: (i) a normalization step to convert application-oriented event data into an application-independent normalized table; (ii) an algorithm for finding significant period lengths from normalized events (e.g., 30 seconds) using a Chi-squared test; and (iii) an algorithm for finding a partially periodic temporal association (e.g., port-down followed by port-up) given a know period.
摘要翻译：提供了用于发现部分周期性时间关联的系统和方法，这里称为p模式。例如，计算机网络中的p模式可能包含五次重复，每隔30秒进行一次停机事件，随后是一个端口事件，随后又是一个随机的间隙，直到这些事件的下一个5次重复。在一个实施例中，本发明包括：（i）将面向应用的事件数据转换成与应用无关的标准化表的归一化步骤; （ii）使用卡方检验从归一化事件（例如，30秒）发现重要的周期长度的算法; 以及（iii）用于在给定知道周期的情况下找到部分周期性时间关联（例如，关闭后端口）的算法。

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