公开(公告)号：US09349150B2

公开(公告)日：2016-05-24

申请号：US14140640

申请日：2013-12-26

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii

IPC: G06N5/02 ,  G08G1/01 ,  G06Q50/28 ,  G06Q10/04 ,  G06Q50/26

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。

公开(公告)号：US20160123748A1

公开(公告)日：2016-05-05

申请号：US14533310

申请日：2014-11-05

Applicant: XEROX CORPORATION

Inventor： Boris Chidlovskii

IPC: G01C21/34

CPC classification number: G01C21/3453 ,  G01C21/3484 ,  G06Q10/02

Abstract: A method and system are disclosed for re-ranking trips from a journey planner using real traveler preferences. A trip request is received that includes an origin, a destination and a departure time. An associated journey planner retrieves a list of candidate trips that correspond to the request. A ranking function, ascertained from actual trips that match the trip request and from which are determined real-world traveler preferences, is applied to the list of candidate trips output by the journey planner, thereby re-ranking the list of candidate trips to reflect real-world traveler's experiences.

Abstract translation: 公开了一种方法和系统，用于使用真正的旅行者偏好从旅程计划员重新排列旅行。 收到包括起点，目的地和出发时间的旅行请求。 相关联的旅程计划器检索与请求相对应的候选行程列表。 从旅行请求实际旅行中确定的，从确定的真实世界旅行者偏好确定的排名功能被应用于由旅程计划者输出的候选旅行列表，从而重新排列候选行程列表以反映真实的 世界旅行者的经验。

公开(公告)号：US20160078359A1

公开(公告)日：2016-03-17

申请号：US14504837

申请日：2014-10-02

Applicant: Xerox Corporation

Inventor： Gabriela Csurka ,  Boris Chidlovskii ,  Florent C. Perronnin

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N7/005 ,  G06K9/6215 ,  G06K9/6262 ,  G06K9/6272 ,  G06N20/00

Abstract: A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.

Abstract translation: 分类系统包括存储器，用于针对一组类别中的每一个存储用于将类概率分配给来自目标域的测试样本的分类器模型。 已经通过来自目标域和至少一个源域的训练样本学习了分类器模型。 每个分类器模型将各个类作为组件的混合模型，组件混合包括每个源域的组件和目标域的组件。 每个组件是测试样本与由类别标记的各个域的训练样本派生的领域特定类别表示之间的距离的函数，混合物中的每个组分由相应的混合物加权 重量。 提供由处理器实施的指令，用于根据分类器模型分配的类概率标记测试样本。

公开(公告)号：US20150346326A1

公开(公告)日：2015-12-03

申请号：US14287538

申请日：2014-05-27

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka ,  Jose Antonio Rodriguez Serrano

IPC: G01S7/48 ,  G01S17/88 ,  G01S17/08

CPC classification number: G01S17/88 ,  G01S7/4802 ,  G01S17/42 ,  G01S17/89 ,  G06K9/00805 ,  G06K9/6206 ,  G06T7/00 ,  G06T7/30 ,  G06T2207/10028

Abstract: A system and method for classifying vehicles from laser scan data by receiving laser scan data corresponding to multiple vehicles from a laser scanner; extracting vehicle shapes corresponding to the multiple vehicles based on the laser scan data; aligning the vehicle shapes; and generating vehicle profiles based on the aligned vehicle shapes. The system and method can further include aligning the vehicle shapes using sequence kernels, such as global alignment kernels, and constraining the sequence kernels based on determined weights.

Abstract translation: 一种用于通过从激光扫描仪接收对应于多个车辆的激光扫描数据来对车辆进行激光扫描数据分类的系统和方法; 基于所述激光扫描数据提取与所述多个车辆相对应的车辆形状; 对准车辆形状; 以及基于对齐的车辆形状产生车辆轮廓。 系统和方法还可以包括使用诸如全局对准内核之类的序列内核来对齐车辆形状，并且基于确定的权重来约束序列内核。

公开(公告)号：US20140089036A1

公开(公告)日：2014-03-27

申请号：US13627371

申请日：2012-09-26

Applicant: XEROX CORPORATION

Inventor： Boris Chidlovskii

IPC: G06Q10/06

CPC classification number: G06Q10/06 ,  G06Q10/047 ,  G06Q10/06315 ,  G06Q50/30

Abstract: A system and method for dynamic zoning are provided. Travel demand data is received for a network which includes a set of points. The travel demand data includes values representing demand from each point to each of other point. Destination-distance values are computed which reflect the similarity between points in a respective pair, based on the travel demand data. For each pair of the points, a geo-distance value is generated which reflects the distance between locations of the points in the pair. An aggregated affinity matrix is formed by aggregating the computed geo-distance values and destination-distance values. The aggregated affinity matrix is used by a clustering algorithm to assign each of the points in the set to a respective one of a set of clusters. A representation of the clusters can be generated in which each of a set of zones encompasses the points assigned to its respective cluster.

Abstract translation: 提供了一种用于动态分区的系统和方法。 对于包括一组点的网络接收旅行需求数据。 旅行需求数据包括表示从每个点到每个其他点的需求的值。 基于旅行需求数据计算反映相应对中的点之间的相似性的目的地距离值。 对于每对点，生成地理距离值，其反映该对中的点的位置之间的距离。 通过聚合计算的地理距离值和目的地距离值来形成聚合亲和度矩阵。 聚合亲和矩阵由聚类算法使用，以将集合中的每个点分配给一组集群中的相应一个。 可以生成集群的表示，其中一组区域中的每一个区域包括分配给其相应集群的点。

公开(公告)号：US20180253627A1

公开(公告)日：2018-09-06

申请号：US15450620

申请日：2017-03-06

Applicant: Xerox Corporation

Inventor： Fabien Baradel ,  Boris Chidlovskii ,  Gabriela Csurka

IPC: G06K9/62

CPC classification number: G06K9/00624 ,  G06K9/4628 ,  G06K9/6262 ,  G06K9/6271

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.

公开(公告)号：US20170147944A1

公开(公告)日：2017-05-25

申请号：US14950544

申请日：2015-11-24

Applicant: Xerox Corporation

Inventor： Gabriela Csurka ,  Boris Chidlovskii ,  Stéphane Clinchant

IPC: G06N99/00 ,  G06N5/02

CPC classification number: G06N99/005 ,  G06N3/0454 ,  G06N3/084 ,  G06N5/02

Abstract: A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.

公开(公告)号：US20170132544A1

公开(公告)日：2017-05-11

申请号：US14936917

申请日：2015-11-10

Applicant: Xerox Corporation

Inventor： Sofia Zaourar Michel ,  Boris Chidlovskii

IPC: G06Q10/06 ,  G06Q50/30

CPC classification number: G06Q10/06313 ,  G06Q50/30

Abstract: Methods, systems, and processor-readable media for the stochastic optimization of public transport schedules. A real-world collection of transit instances can be derived from a transport system and fed as input to a two-stage stochastic program. A schedule offset can be relaxed in the two-stage stochastic program to allow the two-stage stochastic program to operate according to the real-world collection of transit instances. An optimized transport schedule can then be derived from the two-stage stochastic program for use by the transport system based on the schedule offset and the real-world collection of transit instances.

公开(公告)号：US10089640B2

公开(公告)日：2018-10-02

申请号：US14632207

申请日：2015-02-26

Applicant: Xerox Corporation

Inventor： Cesar Roberto de Souza ,  Boris Chidlovskii ,  Frederic Roulland ,  Luis Rafael Ulloa Paredes

IPC: G07B15/00 ,  G07B15/02 ,  G06Q30/02 ,  G06F17/30

Abstract: Methods and systems for interpretable user behavior profiling in off-street parking applications. To render user profiles easy to interpret by decision makers, the semi-automatic discovery and tagging of user profiles can be implemented. Transaction data from one or more (and geographically close) off-street parking installations can be implemented. An analysis of spatio-temporal behavioral patterns can be implemented based on representation of any parking episode by a set of heterogeneous features, the use of clustering methods for automatic pattern discovery, an assessment of obtained clusters, semi-automatic identification/tagging of space-temporal patterns, and a user-friendly interpretation of obtained patterns.

公开(公告)号：US09710729B2

公开(公告)日：2017-07-18

申请号：US14477215

申请日：2014-09-04

Applicant: Xerox Corporation

Inventor： Boris Chidlovskii ,  Gabriela Csurka

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/66 ,  G06K9/46

CPC classification number: G06K9/6259 ,  G06K9/00624 ,  G06K9/00771 ,  G06K9/00785 ,  G06K9/46 ,  G06K9/62 ,  G06K9/6217 ,  G06K9/6256 ,  G06K9/6257 ,  G06K9/6267 ,  G06K9/627 ,  G06K9/6288

Abstract: In camera-based object labeling, boost classifier ƒT(x)=Σr=1Mβrhr(x) is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS1, . . . , DSN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights βr. The rth iteration of the AdaBoost algorithm trains candidate base classifiers hrk(x) each trained on a training set DT∪DSk, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT may be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.