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检索条件"机构=Information Systems and Machine Learning Lab"
128 条 记 录,以下是1-10 订阅
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GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping
arXiv
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arXiv 2025年
作者: Jose, Allen Isaac Pan, Sicong Zaenker, Tobias Menon, Rohit Houben, Sebastian Bennewitz, Maren Bonn-Rhein-Sieg University of Applied Sciences Germany Humanoid Robots Lab University of Bonn Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Germany Humanoid Robots Lab University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany
Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their... 详细信息
来源: 评论
A novel multidimensional framework for evaluating recommender systems
A novel multidimensional framework for evaluating recommende...
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ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender systems and their Interfaces, UCERSTI 2010
作者: Krohn-Grimberghe, Artus Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, suc... 详细信息
来源: 评论
Automatic frankensteining: Creating complex ensembles autonomously  17
Automatic frankensteining: Creating complex ensembles autono...
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17th SIAM International Conference on Data Mining, SDM 2017
作者: Wistuba, Martin Schilling, Nicolas Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners. Therefore, it is not sur... 详细信息
来源: 评论
Atribute-aware non-linear co-embeddings of graph features  13
Atribute-aware non-linear co-embeddings of graph features
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13th ACM Conference on Recommender systems, RecSys 2019
作者: Rashed, Ahmed Grabocka, Josif Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation ... 详细信息
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Geo-ML @ MediaEval placing task 2015
Geo-ML @ MediaEval placing task 2015
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Multimedia Benchmark Workshop, MediaEval 2015
作者: Duong-Trung, Nghia Wistuba, Martin Drumond, Lucas Rego Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
We participated in the MediaEval Benchmarking whose goal is to concentrate on the multimodal geo-location prediction on the Yahoo! Flickr Creative Commons 100M dataset - the placing task. It challenges participants to...
来源: 评论
Move prediction in go-modelling feature interactions using latent factors
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36th Annual German Conference on Artificial Intelligence, KI 2013
作者: Wistuba, Martin Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
Move prediction systems have always been part of strong Go programs. Recent research has revealed that taking interactions between features into account improves the performance of move predictions. In this paper, a f... 详细信息
来源: 评论
A novel multidimensional framework for evaluating recommender systems
A novel multidimensional framework for evaluating recommende...
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Workshop on learning, Knowledge, and Adaptivity, LWA 2010
作者: Krohn-Grimberghe, Artus Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, suc... 详细信息
来源: 评论
Active learning for technology enhanced learning
Active learning for technology enhanced learning
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Symposium on learning, Knowledge, and Adaptivity 2011, LWA 2011
作者: Krohn-Grimberghe, Artus Busche, Andre Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Germany
Suggesting tasks and learning resources of appropriate difficulty to learners is challenging. Neither should they be too difficult and nor too easy. Well-chosen tasks would enable a quick assessment of the learner, we... 详细信息
来源: 评论
IQ estimation for accurate time-series classification
IQ estimation for accurate time-series classification
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IEEE Symposium on Computational Intelligence and Data Mining
作者: Buza, Krisztian Nanopoulos, Alexandros Schmidt-Thieme, Lars Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany
Due to its various applications, time-series classification is a prominent research topic in data mining and computational intelligence. The simple k-NN classifier using dynamic time warping (DTW) distance had been sh... 详细信息
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Factorization techniques for student performance classification and ranking
Factorization techniques for student performance classificat...
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20th Conference on User Modeling, Adaptation, and Personalization, UMAP 2012
作者: Drumond, Lucas Thai-Nghe, Nguyen Horváth, Tomas Schmidt-Thieme, Lars Information Systems and Machine Learning Lab. University of Hildesheim Germany
Historically, student performance prediction has been approached with regression models. For instance, the KDD Cup 2010 used the root mean squared error (RMSE) as an evaluation criterion. This is appropriate when the ... 详细信息
来源: 评论