+

WO2018187948A1 - Procédé de réparation locale pour modèle d'apprentissage machine - Google Patents

Procédé de réparation locale pour modèle d'apprentissage machine Download PDF

Info

Publication number
WO2018187948A1
WO2018187948A1 PCT/CN2017/080172 CN2017080172W WO2018187948A1 WO 2018187948 A1 WO2018187948 A1 WO 2018187948A1 CN 2017080172 W CN2017080172 W CN 2017080172W WO 2018187948 A1 WO2018187948 A1 WO 2018187948A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
machine learning
model
learning model
patch
Prior art date
Application number
PCT/CN2017/080172
Other languages
English (en)
Chinese (zh)
Inventor
邹霞
Original Assignee
邹霞
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 邹霞 filed Critical 邹霞
Priority to PCT/CN2017/080172 priority Critical patent/WO2018187948A1/fr
Publication of WO2018187948A1 publication Critical patent/WO2018187948A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present invention relates to a local repair method of a machine learning model, and belongs to the field of Internet search.
  • search engines have become an important tool for people to use Internet information resources.
  • search engines such as Google, Yahoo!. Bing, and Baidu
  • the relevance of query results has attracted more and more attention.
  • the pros and cons of sorting the results of the query have also become the main indicators for evaluating the search engine.
  • the user gives the keyword as a query request
  • the search engine queries the index database according to the user query, and returns the retrieval result of the sorting and correlation analysis to the user, helping the person to reject and ignore a large amount of irrelevant information, thereby Play the role of information navigation.
  • the massive amount of information data means massive search results.
  • most users of the cable engine only browse the first few pages of the returned results, and rarely care about the lower ranked pages. Search results with strong correlation should be ranked higher, while weak correlation results should be ranked lower. Therefore, sorting the query results according to their relevance becomes one of the core problems of search engines. The relevance ranking of search results has also become an important indicator for evaluating search engine performance.
  • a multidimensional feature vector is used to represent the relevant attributes and information of each data pair (user query-query result). Extract some data pairs in the dataset and manually identify the relevance of the query results and user queries in each data pair.
  • the machine learning model is trained using the already identified data as a training data set, and the resulting machine learning model is used to predict the relevance of the unknown query and the query results.
  • the size of the feedback data set is much smaller than the original training data.
  • the learning model established by re-learning is mainly determined by the original training data set, so the space for performance improvement is very limited.
  • the purpose of the present invention is to modify the original machine learning model from a local perspective, to make up for the deficiencies of the retraining model, incremental learning, and the like, and to improve the performance of the machine learning model.
  • the present invention adopts the following technical solutions:
  • the present invention provides a partial repair method of a machine learning model, comprising the following steps:
  • Step 1 Collecting and analyzing feedback data: collecting user feedback data, and extracting data samples of prediction errors
  • Step 2 Spatial Transformation: Converting the original data space to a new data space through scale learning, in the new In the data space, the distance between the predicted error data samples is reduced as much as possible, and the distance between the predicted error data sample and the predicted correct data sample is increased as much as possible;
  • Step 3 In the new data space, learn the wrong data sample to establish a patch model, and define the application scope of the patch model;
  • Step 4 In the new data space, learn the wrong data sample to establish a patch model, and define the application scope of the patch model.
  • the user feedback data is a series of data pairs, and the result is evaluated by establishing a machine learning model to evaluate the degree of relevance.
  • the spatial distance between the predicted error data samples is reduced as much as possible, and the distance between the predicted error data sample and the predicted correct data sample is exhausted. Possible increase.
  • step 3 after mapping the predicted error data set to the new feature space, a patch model is established on the learning data sample.
  • the process of establishing the patch model in the above step 3 is a training process of the supervised machine learning model.
  • the machine learning model is used to predict the ordering of the query results.
  • the local repair method of the machine learning model does not change the original learning model, but only learns the sub-space of the local patch of the model and the patch model according to the predicted error data fed back by the user, and the original learning
  • the model and the generated patch model form a new learning model, modify the original machine learning model from a local perspective, make up for the shortcomings of retraining, incrementing, etc., and improve the performance of the machine learning model.
  • the present invention provides a method for locally repairing a machine learning model.
  • the present invention will be further described in detail in the following embodiments in order to clarify and clarify the objects, technical solutions and effects of the present invention. It should be understood that The specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
  • the machine learning model In the process of processing massive amounts of information, the machine learning model has been widely used in various problems and played a huge role with its automatic and rapid advantages.
  • Machine learning models especially supervised machine learning models, are supported by a large amount of training data to achieve higher and higher prediction accuracy.
  • the machine learning model has some drawbacks. Once the machine learning model is built, it is like a black box, only the input and output are visible. Even if you find data that predicts errors, you cannot adjust the original machine learning model. Moreover, no matter how powerful a machine learning model is, there is no guarantee that its prediction accuracy will be 100%. This requires constant adjustment of the original machine learning model based on the user's feedback data to continuously improve the prediction accuracy.
  • the machine learning model addressed by this embodiment is a collection of multiple decision trees.
  • a decision tree represents a submodel.
  • the weighted sum of the prediction results of all submodels is the final prediction result.
  • For each user query a collection of query results.
  • a feature vector is used to represent each query result.
  • the non-leaf node will calculate some attributes of the query result, and determine the path of the query result in the current decision tree according to the set threshold. When the leaf node is reached, it can be obtained.
  • the classification result of the query result The classification result is represented by a score.
  • the final result of the query result is obtained by weighted summation of the classification results of the query results on each decision tree.
  • the level of the score determines how relevant the query results are to the user's query. The higher the score, the stronger the correlation; the lower the score, the weaker correlation.
  • the feedback data information of the user is first collected, and the prediction error samples therein are extracted. Learning and training the prediction error samples, and establishing a patch model to make up for the defects of the original model. In correcting the peers of the predicted error samples, it must be ensured that there is no negative impact on predicting the correct data. Therefore, in the local repair method, not only the patch model needs to be established, but also the scope of the patch model application needs to be defined.
  • the data space of the prediction failure is spatially transformed into a new space by the method of scale learning, and in the new data space.
  • the data samples that failed the prediction are aggregated as much as possible, and away from predicting the correct data samples.
  • a patch model is created. The patch model is obtained by learning and training the data samples that failed to predict. After the patch model is built, the peers also need to define the area in which the patch model is applied.
  • the local repair method provided by this embodiment is mainly divided into the following four steps:
  • User feedback data D is composed of a series of data pairs.
  • the machine learning model can be used to evaluate the result of the query d / (represented. For any two pairs of data ( ⁇ q, d i> , r t ), ( ⁇ q, dj>, r 7 r , ⁇ rj ( ⁇ i; ) > f(dj), Bay U considers ( ⁇ ;; , r; ), ( ⁇ q , dj>, rj ) is a pair of data pairs that predict errors.
  • the spatial transformation matrix is learned.
  • the feature space of the original data needs to be transformed.
  • the purpose of the spatial transformation is to make the spatial distance between the predicted error data samples as small as possible in the new feature space, and the distance between the predicted error data sample and the predicted correct data sample is increased as much as possible. . This minimizes the impact of the patch model on predicting correct data, thereby ensuring the predictive accuracy of the new machine learning model.
  • the two query results have different correlations with the user query, and the patch model is continuously updated by analyzing the two query results.
  • the calculation formula is as shown in (1):
  • the space transformation is first required to obtain a new spatial feature vector A, ⁇ , and then the new machine learning model is used to learn A ⁇ Forecast, get the final evaluation score.
  • the query results are sorted according to the scores.
  • This embodiment uses the method of scale learning to map the original data into a new feature space.
  • the objective function only considers data samples of prediction errors in the data space. This is because the model repair algorithm is mainly used to repair the data samples of the prediction errors, but it does not need to be processed for predicting the correct data samples. Moreover, in the user feedback data set, the size of the predicted error data sample is much smaller than the predicted correct data sample size. Considering only the data samples that predict errors will greatly improve the efficiency of the algorithm.
  • the local repair method of the machine learning model does not change the original learning model, but only learns the sub-space of the local patch of the model and the patch model according to the predicted error data fed back by the user, and the original learning
  • the model and the generated patch model form a new learning model, modify the original machine learning model from a local perspective, make up for the shortcomings of retraining, incrementing, etc., and improve the performance of the machine learning model.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé de réparation locale pour un modèle d'apprentissage machine, comprenant : la collecte et l'analyse de données de rétroaction : collecte de données de rétroaction d'utilisateur et extraction d'échantillons de données incorrectement prédits ; la transformation spatiale : conversion d'un espace de données d'origine en un nouvel espace de données au moyen d'un apprentissage d'échelle, réduction autant que possible de la distance entre les échantillons de données incorrectement prédits dans le nouvel espace de données, et augmentation autant que possible de la distance entre les échantillons de données incorrectement prédits et les échantillons de données correctement prédits ; l'apprentissage d'échantillons de données incorrectes pour établir un modèle de correctif dans le nouvel espace de données et définir une plage d'application du modèle de correctif ; et l'apprentissage des échantillons de données incorrectes pour établir un modèle de correctif dans le nouvel espace de données et la définition d'une plage d'application du modèle de correctif. Le procédé de réparation locale pour le modèle d'apprentissage machine peut améliorer les performances du modèle d'apprentissage machine.
PCT/CN2017/080172 2017-04-12 2017-04-12 Procédé de réparation locale pour modèle d'apprentissage machine WO2018187948A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080172 WO2018187948A1 (fr) 2017-04-12 2017-04-12 Procédé de réparation locale pour modèle d'apprentissage machine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/080172 WO2018187948A1 (fr) 2017-04-12 2017-04-12 Procédé de réparation locale pour modèle d'apprentissage machine

Publications (1)

Publication Number Publication Date
WO2018187948A1 true WO2018187948A1 (fr) 2018-10-18

Family

ID=63793095

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/080172 WO2018187948A1 (fr) 2017-04-12 2017-04-12 Procédé de réparation locale pour modèle d'apprentissage machine

Country Status (1)

Country Link
WO (1) WO2018187948A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120263376A1 (en) * 2011-04-12 2012-10-18 Sharp Laboratories Of America, Inc. Supervised and semi-supervised online boosting algorithm in machine learning framework
CN103150578A (zh) * 2013-04-09 2013-06-12 山东师范大学 一种基于半监督学习的支持向量机分类器训练方法
CN104572998A (zh) * 2015-01-07 2015-04-29 北京云知声信息技术有限公司 用于自动问答系统的问答排序模型更新方法及装置
US20160162779A1 (en) * 2014-12-05 2016-06-09 RealMatch, Inc. Device, system and method for generating a predictive model by machine learning
CN106548210A (zh) * 2016-10-31 2017-03-29 腾讯科技（深圳）有限公司 机器学习模型训练方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120263376A1 (en) * 2011-04-12 2012-10-18 Sharp Laboratories Of America, Inc. Supervised and semi-supervised online boosting algorithm in machine learning framework
CN103150578A (zh) * 2013-04-09 2013-06-12 山东师范大学 一种基于半监督学习的支持向量机分类器训练方法
US20160162779A1 (en) * 2014-12-05 2016-06-09 RealMatch, Inc. Device, system and method for generating a predictive model by machine learning
CN104572998A (zh) * 2015-01-07 2015-04-29 北京云知声信息技术有限公司 用于自动问答系统的问答排序模型更新方法及装置
CN106548210A (zh) * 2016-10-31 2017-03-29 腾讯科技（深圳）有限公司 机器学习模型训练方法及装置

Similar Documents

Publication Publication Date Title
Liu et al. ProtDec-LTR3. 0: protein remote homology detection by incorporating profile-based features into learning to rank
Wu et al. AutoCTS+: Joint neural architecture and hyperparameter search for correlated time series forecasting
CN111275172A (zh) 一种基于搜索空间优化的前馈神经网络结构搜索方法
CN106528874A (zh) 基于Spark内存计算大数据平台的CLR多标签数据分类方法
CN111339258B (zh) 基于知识图谱的大学计算机基础习题推荐方法
CN109857457B (zh) 一种在双曲空间中学习源代码中的函数层次嵌入表示方法
CN107330557A (zh) 一种基于社区划分和熵的舆情热点跟踪及预测方法与装置
CN116306923A (zh) 一种基于知识图谱的评价权重计算方法
CN118656698A (zh) 一种多元历史文化资源的分类方法及设备
CN112965968A (zh) 一种基于注意力机制的异构数据模式匹配方法
CN111898579A (zh) 基于极限梯度提升的高分遥感影像无偏半监督分类模型
CN110442736B (zh) 一种基于二次判别分析的语义增强子空间跨媒体检索方法
CN118861996B (zh) 多源异构数据融合方法、装置以及存储介质
CN118690260B (zh) 一种基于深度学习的大数据信息处理方法、系统、介质及服务器
CN118861992A (zh) 一种智能制造的多模态数据处理方法、装置、设备及介质
CN113269310A (zh) 基于反事实的图神经网络可解释性方法
CN109543712B (zh) 时态数据集上的实体识别方法
CN116821407A (zh) 基于hgr最大相关性的跨模态遥感图像检索方法及装置
WO2018187948A1 (fr) Procédé de réparation locale pour modèle d'apprentissage machine
CN117056226A (zh) 基于迁移学习的跨项目软件缺陷数量预测方法
CN110032677A (zh) 一种基于深度神经网络和概率矩阵分解的混合推荐算法
CN117689865A (zh) 一种基于特征和融合模式搜索的目标检测方法及系统
CN114238439A (zh) 一种基于联合嵌入的任务驱动关系型数据视图推荐方法
CN111755074A (zh) 一种酿酒酵母菌中dna复制起点的预测方法
Zhang et al. Towards fine-scale population stratification modeling based on kernel principal component analysis and random forest

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17905755

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS (EPO FORM 1205A DATED 21.02.2020)

122 Ep: pct application non-entry in european phase

Ref document number: 17905755

Country of ref document: EP

Kind code of ref document: A1

点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载