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1. CN114072820 - Executing machine-learning models

Office
Chine
Numéro de la demande 201980097196.1
Date de la demande 04.06.2019
Numéro de publication 114072820
Date de publication 18.02.2022
Type de publication A
CIB
G06N 20/00
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
20Apprentissage automatique
G06N 3/04
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
3Systèmes de calculateurs basés sur des modèles biologiques
02utilisant des modèles de réseaux neuronaux
04Architecture, p.ex. topologie d'interconnexion
G06N 5/02
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
5Systèmes de calculateurs utilisant des modèles basés sur la connaissance
02Représentation de la connaissance
CPC
G06N 20/20
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
20Ensemble learning
G06N 5/003
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
003Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines [SVM]
G06N 5/043
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
043Distributed expert systems; Blackboards
Déposants TELEFONAKTIEBOLAGET LM ERICSSON
瑞典爱立信有限公司
Inventeurs SUN BIN
B·孙
INAM RAFIA
R·伊纳姆
VULGARAKIS FELJAN ANETA
A·沃尔加拉基斯菲尔詹
VANDIKAS KONSTANTINOS
K·范迪卡斯
Mandataires 北京市中咨律师事务所 11247
Titre
(EN) Executing machine-learning models
(ZH) 执行机器学习模型
Abrégé
(EN) Embodiments described herein provides methods and apparatus for executing a machine-learning model. A first machine-learning model, based on a first set of data and using a machine-learning algorithm, is developed at a first node. A second machine-learning model, based on the first machine-learning model and a second set of data, and using the machine-learning algorithm, is developed at a second node. Information about a difference between the first machine-learning model and the second machine-learning model is communicated from the second node to the first node. A request for execution of a machine-learning model is received at the first node. Responsive to receiving the request for the execution of the machine-learning model, information indicative of an execution policy is obtained at the first node. Finally, depending on the obtained information indicative of an execution policy, either, at the first node, a machine-learning model based on the first machine-learning model and the information about a difference between the first machine-learning model and the second machine-learning model is executed to obtain a result; or the first machine-learning model is partially executed at the first node, and the second machine-learning model is partially executed at the second node, to obtain a result.
(ZH) 本文所描述的实施例提供了用于执行机器学习模型的方法和装置。在第一节点处,基于第一数据集并使用机器学习算法开发第一机器学习模型。在第二节点处,基于第一机器学习模型和第二数据集并使用机器学习算法开发第二机器学习模型。关于第一机器学习模型与第二机器学习模型之间的差异的信息从第二节点被传送到第一节点。在第一节点处接收对执行机器学习模型的请求。响应于接收到对执行机器学习模型的请求,在第一节点处获得指示执行策略的信息。最后,取决于所获得的指示执行策略的信息:在第一节点处执行基于第一机器学习模型和关于第一机器学习模型与第二机器学习模型之间的差异的信息的机器学习模型以获得结果;或者在第一节点处部分地执行第一机器学习模型,并在第二节点处部分地执行第二机器学习模型,以获得结果。
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