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公开(公告)号：US20170161633A1
公开(公告)日：2017-06-08
申请号：US14960869
申请日：2015-12-07
Applicant: Xerox Corporation
Inventor： Stéphane Clinchant , Gabriela Csurka , Boris Chidlovskii
CPC classification number: G06N20/00 , G06N3/0454
Abstract: A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.
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公开(公告)号：US09519912B2
公开(公告)日：2016-12-13
申请号：US14032476
申请日：2013-09-20
Applicant: Xerox Corporation
Inventor： Boris Chidlovskii , Mohammad Ghufran
CPC classification number: G06Q30/0202 , G06N99/005 , G06Q10/0631
Abstract: In demand prediction, a history of demand for a resource is modeled to generate a baseline model of the demand, and demand for the resource at a prediction time is predicted by evaluating a regression function of depth k operating on an input data set including at least the demand for the resource at the prediction time output by the baseline model and measured demand for the resource measured at k times prior to the prediction time. The resource may be off-street parking, and the input data set may further include weather data. The regression function may comprise a support vector regression (SVR) function that is trained on the history of demand for the resource. The baseline model suitably comprises a Fourier model of the history of demand for the resource.
Abstract translation: 在需求预测中，对资源的需求历史进行建模以生成需求的基准模型，并且通过评估在包括至少包括在内的输入数据集上操作的深度k的回归函数来预测在预测时间对资源的需求 在基准模型输出的预测时间内对资源的需求和在预测时间之前k次测量的资源的测量需求。 资源可以是路边停车场，并且输入数据集还可以包括天气数据。 回归函数可以包括对资源的需求历史训练的支持向量回归（SVR）函数。 基线模型适当地包括资源需求历史的傅里叶模型。
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23.发明申请SYSTEM AND METHOD FOR MULTI-TASK LEARNING FOR PREDICTION OF DEMAND ON A SYSTEM 有权用于多系统学习的系统和方法，用于预测系统中的需求公开(公告)号：US20150186792A1
公开(公告)日：2015-07-02
申请号：US14140640
申请日：2013-12-26
Applicant: Xerox Corporation
Inventor： Boris Chidlovskii
CPC classification number: G06Q50/26 , G06N5/025 , G06Q10/04 , G06Q50/28 , G08G1/0129
Abstract: A multi-task learning system and method for predicting travel demand on an associated transportation network are provided. Observations corresponding to the associated transportation network are collected and a set of time series corresponding to travel demand are generated. Clusters of time series are then formed and for each cluster, multi-task learning is applied to generate a prediction model. Travel demand on a selected segment of the associated transportation network corresponding to at least one of the set of time series is then predicted in accordance with the generated prediction model.
Abstract translation: 提供了一种用于预测相关运输网络上的旅行需求的多任务学习系统和方法。 收集与相关运输网络对应的观测值，并生成一组对应于旅行需求的时间序列。 然后形成时间序列的集群，并且对于每个集群，应用多任务学习来生成预测模型。 然后根据所生成的预测模型来预测与所述一组时间序列中的至少一个对应的相关联的运输网络的所选段的旅行需求。
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公开(公告)号：US20150088790A1
公开(公告)日：2015-03-26
申请号：US14032476
申请日：2013-09-20
Applicant: Xerox Corporation
Inventor： Boris Chidlovskii , Mohammad Ghufran
CPC classification number: G06Q30/0202 , G06N99/005 , G06Q10/0631
Abstract: In demand prediction, a history of demand for a resource is modeled to generate a baseline model of the demand, and demand for the resource at a prediction time is predicted by evaluating a regression function of depth k operating on an input data set including at least the demand for the resource at the prediction time output by the baseline model and measured demand for the resource measured at k times prior to the prediction time. The resource may be off-street parking, and the input data set may further include weather data. The regression function may comprise a support vector regression (SVR) function that is trained on the history of demand for the resource. The baseline model suitably comprises a Fourier model of the history of demand for the resource.
Abstract translation: 在需求预测中，对资源的需求历史进行建模以生成需求的基准模型，并且通过评估在包括至少包括在内的输入数据集上操作的深度k的回归函数来预测在预测时间对资源的需求 在基准模型输出的预测时间内对资源的需求和在预测时间之前k次测量的资源的测量需求。 资源可以是路边停车场，并且输入数据集还可以包括天气数据。 回归函数可以包括对资源的需求历史训练的支持向量回归（SVR）函数。 基线模型适当地包括资源需求历史的傅里叶模型。
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公开(公告)号：US20140156231A1
公开(公告)日：2014-06-05
申请号：US13690071
申请日：2012-11-30
Applicant: XEROX CORPORATION
Inventor： Shengbo Guo , Boris Chidlovskii , Cedric Archambeau , Guillaume Bouchard , Dawei Yin
IPC: G06F17/18
Abstract: A multi-relational data set is represented by a probabilistic multi-relational data model in which each entity of the multi-relational data set is represented by a D-dimensional latent feature vector. The probabilistic multi-relational data model is trained using a collection of observations of relations between entities of the multi-relational data set. The collection of observations includes observations of at least two different relation types. A prediction is generated for an observation of a relation between two or more entities of the multi-relational data set based on a dot product of the optimized D-dimensional latent feature vectors representing the two or more entities. The training may comprise optimizing the D-dimensional latent feature vectors to maximize likelihood of the collection of observations, for example by Bayesian inference performed using Gibbs sampling.
Abstract translation: 多关系数据集由概率多关系数据模型表示，其中多关系数据集的每个实体由D维潜在特征向量表示。 概率多关系数据模型使用多关系数据集的实体之间的关系的观察集来训练。 观察的收集包括至少两种不同关系类型的观察。 生成用于基于代表两个或多个实体的优化的D维潜在特征向量的点积来观察多关系数据集的两个或多个实体之间的关系的预测。 该训练可以包括优化D维潜在特征向量以最大化观察的收集的可能性，例如通过使用吉布斯抽样执行的贝叶斯推理。
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Patent Agency Ranking
公开(公告)号：US10289909B2
公开(公告)日：2019-05-14
申请号：US15450620
申请日：2017-03-06
Applicant: Xerox Corporation
Inventor： Fabien Baradel , Boris Chidlovskii , Gabriela Csurka
Abstract: A method and apparatus for classifying an image. In one example, the method may include receiving one or more images associated with a source domain and one or more images associated with a target domain, identifying one or more source domain features based on the one or more images associated with the source domain, identifying one or more target domain features based on the one or more images associated with the target domain, training a conditional maximum mean discrepancy (CMMD) engine based on a difference between the one or more source domain features and the one or more target domain features, applying the CMMD engine to the one or more images associated with the target domain to generate one or more labels for each unlabeled target image of the one or more images associated with the target domain and classifying each one of the one or more images in the target domain using the one or more labels.
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27.发明申请公开(公告)号：US20180024968A1
公开(公告)日：2018-01-25
申请号：US15216805
申请日：2016-07-22
Applicant: Xerox Corporation
Inventor： Stéphane Clinchant , Gabriela Csurka , Boris Chidlovskii
CPC classification number: G06N3/0454
Abstract: A method for domain adaptation of samples includes receiving training samples from a plurality of domains, the plurality of domains including at least one source domain and a target domain, each training sample including values for a set of features. A domain predictor is learned on at least some of the training samples from the plurality of domains and respective domain labels. Domain adaptation is performed on the training samples using marginalized denoising autoencoding. This generates a domain adaptation transform layer (or layers) that transforms the training samples to a common adapted feature space. The domain adaptation employs the domain predictor to bias the domain adaptation towards one of the plurality of domains. Domain adapted training samples and their class labels can be used to train a classifier for prediction of class labels for unlabeled target samples that have been domain adapted with the domain adaptation transform layer(s).
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公开(公告)号：US09715695B2
公开(公告)日：2017-07-25
申请号：US14726887
申请日：2015-06-01
Applicant: Xerox Corporation
CPC classification number: G06Q30/0202 , G06N7/005
Abstract: Methods and systems for estimating airport usage demand. Airport parking traffic usage data and flight-time table data can be compiled with respect to an airport (or more than one airport). The airport parking traffic usage data and flight-time table data can be analyzed using an efficient time matching approach (e.g., a time segment matching algorithm). An efficient method to match passengers and flights is introduced. Passenger behavior can be estimated with respect to the airport based on the airport parking traffic usage data and flight-time table data.
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29.发明申请公开(公告)号：US20170206201A1
公开(公告)日：2017-07-20
申请号：US15000560
申请日：2016-01-19
Applicant: Xerox Corporation
Inventor： Boris Chidlovskii
CPC classification number: G06F17/3053 , G06F17/16 , G06F17/30867 , G06Q10/025 , G06Q10/047 , G06Q50/30
Abstract: A method and system are disclosed for generating a list of trips on an associated transportation network, the list ranked in accordance with time-dependent modeling of passenger preferences. User preferences of choosing a specific public transportation service or change point are modeled by a set of latent variables. Any actual trip on the network is converted into a set of pairwise preferences implicitly made by the passenger during the trip. Sequences of services matrices and change points matrices from the retrieved set of trips and non-negative factorization of the services and change points matrices is performed to smooth the matrices. The set of pairwise preferences are used to learn a ranking function and the output of a journey planner is re-ranked using the ranking function.
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30.发明申请METHOD, SYSTEM AND PROCESSOR-READABLE MEDIA FOR ESTIMATING AIRPORT USAGE DEMAND 有权用于估算机场使用需求的方法，系统和处理器可读介质公开(公告)号：US20160350770A1
公开(公告)日：2016-12-01
申请号：US14726887
申请日：2015-06-01
Applicant: Xerox Corporation
CPC classification number: G06Q30/0202 , G06N7/005
Abstract: Methods and systems for estimating airport usage demand. Airport parking traffic usage data and flight-time table data can be compiled with respect to an airport (or more than one airport). The airport parking traffic usage data and flight-time table data can be analyzed using an efficient time matching approach (e.g., a time segment matching algorithm). An efficient method to match passengers and flights is introduced. Passenger behavior can be estimated with respect to the airport based on the airport parking traffic usage data and flight-time table data.
Abstract translation: 估算机场使用需求的方法和系统。 可以针对机场（或多于一个机场）编制机场停车交通使用数据和飞行时间表数据。 可以使用有效的时间匹配方法（例如，时间段匹配算法）来分析机场停车交通使用数据和飞行时间表数据。 介绍了一种匹配乘客和航班的有效方法。 可以根据机场停车交通使用数据和飞行时间表数据，估计机场的客运行为。
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