Machine Learning最新文献_第9页

Generalization bounds for learning under graph-dependence: a survey 图依赖性下学习的泛化边界：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-03 DOI: 10.1007/s10994-024-06536-9

Rui-Ray Zhang, Massih-Reza Amini

引用次数: 0

An effective keyword search co-occurrence multi-layer graph mining approach 一种有效的关键词搜索共现多层图挖掘方法

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-04-02 DOI: 10.1007/s10994-024-06528-9

Janet Oluwasola Bolorunduro, Zhaonian Zou, Mohamed Jaward Bah

{"title":"An effective keyword search co-occurrence multi-layer graph mining approach","authors":"Janet Oluwasola Bolorunduro, Zhaonian Zou, Mohamed Jaward Bah","doi":"10.1007/s10994-024-06528-9","DOIUrl":"https://doi.org/10.1007/s10994-024-06528-9","url":null,"abstract":"<p>A combination of tools and methods known as \"graph mining\" is used to evaluate real-world graphs, forecast the potential effects of a given graph’s structure and properties for various applications, and build models that can yield actual graphs that closely resemble the structure seen in real-world graphs of interest. However, some graph mining approaches possess scalability and dynamic graph challenges, limiting practical applications. In machine learning and data mining, among the unique methods is graph embedding, known as network representation learning where representative methods suggest encoding the complicated graph structures into embedding by utilizing specific pre-defined metrics. Co-occurrence graphs and keyword searches are the foundation of search engine optimizations for diverse real-world applications. Current work on keyword searches on graphs is based on pre-established information retrieval search criteria and does not provide semantic linkages. Recent works on co-occurrence and keyword search methods function effectively on graphs with only one layer instead of many layers. However, the graph neural network has been utilized in recent years as a branch of graph model due to its excellent performance. This paper proposes an Effective Keyword Search Co-occurrence Multi-Layer Graph mining method by employing two core approaches: Multi-layer Graph Embedding and Graph Neural Networks. We conducted extensive tests using benchmarks on real-world data sets. Considering the experimental findings, the proposed method enhanced with the regularization approach is substantially excellent, with a 10% increment in precision, recall, and f1-score.</p>","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training data influence analysis and estimation: a survey 培训数据的影响分析和估计：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-29 DOI: 10.1007/s10994-023-06495-7

Zayd Hammoudeh, Daniel Lowd

引用次数: 0

Machine learning with a reject option: a survey 带有拒绝选项的机器学习：一项调查

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-29 DOI: 10.1007/s10994-024-06534-x

Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis

引用次数: 0

Personalization for web-based services using offline reinforcement learning 利用离线强化学习实现网络服务个性化

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-28 DOI: 10.1007/s10994-024-06525-y

Pavlos Athanasios Apostolopoulos, Zehui Wang, Hanson Wang, Tenghyu Xu, Chad Zhou, Kittipate Virochsiri, Norm Zhou, Igor L. Markov

引用次数: 0

Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling? 跨域术语提取能否受益于跨语言转移和嵌套术语标注？

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-27 DOI: 10.1007/s10994-023-06506-7

Hanh Thi Hong Tran, Matej Martinc, Andraz Repar, Nikola Ljubešić, Antoine Doucet, Senja Pollak

{"title":"Can cross-domain term extraction benefit from cross-lingual transfer and nested term labeling?","authors":"Hanh Thi Hong Tran, Matej Martinc, Andraz Repar, Nikola Ljubešić, Antoine Doucet, Senja Pollak","doi":"10.1007/s10994-023-06506-7","DOIUrl":"https://doi.org/10.1007/s10994-023-06506-7","url":null,"abstract":"<p>Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we treat ATE as a sequence-labeling task and explore the efficacy of XLMR in evaluating cross-lingual and multilingual learning against monolingual learning in the cross-domain ATE context. Additionally, we introduce NOBI, a novel annotation mechanism enabling the labeling of single-word nested terms. Our experiments are conducted on the ACTER corpus, encompassing four domains and three languages (English, French, and Dutch), as well as the RSDO5 Slovenian corpus, encompassing four additional domains. Results indicate that cross-lingual and multilingual models outperform monolingual settings, showcasing improved F1-scores for all languages within the ACTER dataset. When incorporating an additional Slovenian corpus into the training set, the multilingual model exhibits superior performance compared to state-of-the-art approaches in specific scenarios. Moreover, the newly introduced NOBI labeling mechanism enhances the classifier’s capacity to extract short nested terms significantly, leading to substantial improvements in Recall for the ACTER dataset and consequentially boosting the overall F1-score performance.</p>","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure discovery in PAC-learning by random projections 通过随机投影发现 PAC-learning 中的结构

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-26 DOI: 10.1007/s10994-024-06531-0

{"title":"Structure discovery in PAC-learning by random projections","authors":"","doi":"10.1007/s10994-024-06531-0","DOIUrl":"https://doi.org/10.1007/s10994-024-06531-0","url":null,"abstract":"<h3>Abstract</h3> <p>High dimensional learning is data-hungry in general; however, many natural data sources and real-world learning problems posses some hidden low-complexity structure that permit effective learning from relatively small sample sizes. We are interested in the general question of how to discover and exploit such hidden benign traits when problem-specific prior knowledge is insufficient. In this work, we address this question through random projection’s ability to expose structure. We study both compressive learning and high dimensional learning from this angle by introducing the notions of compressive distortion and compressive complexity. We give user-friendly PAC bounds in the agnostic setting that are formulated in terms of these quantities, and we show that our bounds can be tight when these quantities are small. We then instantiate these quantities in several examples of particular learning problems, demonstrating their ability to discover interpretable structural characteristics that make high dimensional instances of these problems solvable to good approximation in a random linear subspace. In the examples considered, these turn out to resemble some familiar benign traits such as the margin, the margin distribution, the intrinsic dimension, the spectral decay of the data covariance, or the norms of parameters—while our general notions of compressive distortion and compressive complexity serve to unify these, and may be used to discover benign structural traits for other PAC-learnable problems.</p>","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days 他们何时到来？了解并预测比赛日抵达巴塞罗那足球俱乐部球场的时间安排

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-26 DOI: 10.1007/s10994-023-06499-3

Feliu Serra-Burriel, Pedro Delicado, Fernando M. Cucchietti, Eduardo Graells-Garrido, Alex Gil, Imanol Eguskiza

{"title":"When are they coming? Understanding and forecasting the timeline of arrivals at the FC Barcelona stadium on match days","authors":"Feliu Serra-Burriel, Pedro Delicado, Fernando M. Cucchietti, Eduardo Graells-Garrido, Alex Gil, Imanol Eguskiza","doi":"10.1007/s10994-023-06499-3","DOIUrl":"https://doi.org/10.1007/s10994-023-06499-3","url":null,"abstract":"<p>Futbol Club Barcelona operates the largest stadium in Europe (with a seating capacity of almost one hundred thousand people) and manages recurring sports events. These are influenced by multiple conditions (time and day of the week, weather, adversary) and affect city dynamics—e.g., peak demand for related services like public transport and stores. We study fine grain audience entrances at the stadium segregated by visitor type and gate to gain insights and predict the arrival behavior of future games, with a direct impact on the organizational performance and productivity of the business. We can forecast the timeline of arrivals at gate level 72 h prior to kickoff, facilitating operational and organizational decision-making by anticipating potential agglomerations and audience behavior. Furthermore, we can identify patterns for different types of visitors and understand how relevant factors affect them. These findings directly impact commercial and business interests and can alter operational logistics, venue management, and safety.</p>","PeriodicalId":49900,"journal":{"name":"Machine Learning","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bounding the Rademacher complexity of Fourier neural operators 限制傅立叶神经算子的拉德马赫复杂性

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-26 DOI: 10.1007/s10994-024-06533-y

Taeyoung Kim, Myungjoo Kang

引用次数: 0

Ijuice: integer JUstIfied counterfactual explanations Ijuice：整数化的反事实解释

IF 7.5 3区计算机科学

Machine Learning Pub Date : 2024-03-26 DOI: 10.1007/s10994-024-06530-1

Alejandro Kuratomi, Ioanna Miliou, Zed Lee, Tony Lindgren, Panagiotis Papapetrou

引用次数: 0