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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 complex structures, which often result in fruit occlusions. Existing view planning methods attempt to reduce occlusions but either struggle to achieve adequate coverage or incur high robot motion costs. We introduce a global optimization approach for view motion planning that aims to minimize robot motion costs while maximizing fruit coverage. To this end, we leverage coverage constraints derived from the set covering problem (SCP) within a shortest Hamiltonian path problem (SHPP) formulation. While both SCP and SHPP are well-established, their tailored integration enables a unified framework that computes a global view path with minimized motion while ensuring full coverage of selected targets. Given the NP-hard nature of the problem, we employ a region-prior-based selection of coverage targets and a sparse graph structure to achieve effective optimization outcomes within a limited time. Experiments in simulation demonstrate that our method detects more fruits, enhances surface coverage, and achieves higher volume accuracy than the motion-efficient baseline with a moderate increase in motion cost, while significantly reducing motion costs compared to the coverage-focused baseline. Real-world experiments further confirm the practical applicability of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Fruits

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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, such as accuracy, robustness, and scalability. In related research, usually only condensed numeric scores such as RMSE or AUC or F-measure are used for evaluation of an algorithm on a given data set. It is obvious that these scores are insufficient to measure user satisfaction. Focussing on the requirements of business and research users, this work proposes a novel, extensible framework for the evaluation of recommender systems. In order to ease user-driven analysis we have chosen a multidimensional approach. The research framework advocates interactive visual analysis, which allows easy refining and reshaping of queries. Integrated actions such as drill-down or slice/dice, enable the user to assess the performance of recommendations in terms of business criteria such as increase in revenue, accuracy, prediction error, coverage and more. The ability of the proposed framework to comprise an effective way for evaluating recommender systems in a business- user-centric way is shown by experimental results using a research prototype.

关键词： Data warehouses

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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

ISBN: (纸本)9781611974874

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 surprising that automated machine learning has become a very interesting field of research. While current research is mainly focusing on finding good pairs of algorithms and hyperparameter configurations, we will present an approach that automates the process of creating a top performing ensemble of several layers, different algorithms and hyperparameter configurations. These kinds of ensembles are called jokingly Frankenstein ensembles and proved their benefit on versatile data sets in many machine learning challenges. We compare our approach Automatic Frankensteining with the current state of the art for automated machine learning on 80 different data sets and can show that it outperforms them on the majority using the same training time. Furthermore, we compare Automatic Frankensteining on a large scale data set to more than 3,500 machine learning expert teams and are able to outperform more than 3,000 of them within 12 CPU hours. Copyright © by SIAM.

关键词： Artificial intelligence

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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

ISBN: (纸本)9781450362436

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 accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec signifcantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information. © 2019 Copyright held by the owner/author(s).

关键词： Recommender systems

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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 develop models and/or techniques to estimate the geographic locations of the Flickr resources based on textual metadata, e.g. titles, descriptions and tags. We aim to find a procedure that is conceptual to understand, simple to implement and flexible to integrate different techniques. In this paper, we present a three-step approach to tackle the locale-based sub-task.

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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

ISBN: (纸本)9783642409417

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 factorization model is applied and a supervised learning algorithm, Latent Factor Ranking (LFR), which enables to consider these interactions, is introduced. Its superiority will be demonstrated in comparison to other state-of-the-art Go move predictors. LFR improves accuracy by 3% over current state-of-the-art Go move predictors on average and by 5% in the middle-and endgame of a game. Depending on the dimensionality of the shared, latent factor vector, an overall accuracy of over 41% is achieved. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Forecasting

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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, such as accuracy, robustness, and scalability. In related research, usually only condensed numeric scores such as RMSE or AUC or F-measure are used for evaluation of an algorithm on a given data set. It is obvious that these scores are insufficient to measure user satisfaction. Focussing on the requirements of business and research users, this work proposes a novel, extensible framework for the evaluation of recommender systems. In order to ease user-driven analysis we have chosen a multidimensional approach. The research framework advocates interactive visual analysis, which allows easy refining and reshaping of queries. Integrated actions such as drill-down or slice/dice, enable the user to assess the performance of recommendations in terms of business criteria such as increase in revenue, accuracy, prediction error, coverage and more. The ability of the proposed framework to comprise an effective way for evaluating recommender systems in a businessuser- centric way is shown by experimental results using a research prototype.

关键词： Data warehouses

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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, well-chosen learning resources would speed up the learning curve most. We connect active learning to classical pedagogical theory and propose the uncertainty sampling framework as a means to the challenge of selecting optimal tasks and learning resources to learners. To assess the efficiency of this strategy, we compared different exercise selection strategies and evaluated their effect on different datasets. We consistently find that uncertainty sampling significantly outperforms several alternative exercise selection approaches and thus leads to a faster convergence to the true assessment. These findings demonstrate that active (machine) learning is consistent with classic learning theory. It is a valuable instrument for choosing appropriate exercises as well as learning resources both from a teacher's and from a learner's perspective.

关键词： Teaching

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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

ISBN: (纸本)9781424499274

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 shown to be competitive to other state-of-the art time-series classifiers. In our research, however, we observed that a single fixed choice for the number of nearest neighbors k may lead to suboptimal performance. This is due to the complexity of time-series data, especially because the characteristic of the data may vary from region to region. Therefore, local adaptations of the classification algorithm is required. In order to address this problem in a principled way by, in this paper we introduce individual quality (IQ) estimation. This refers to estimating the expected classification accuracy for each time series and each k individually. Based on the IQ estimations we combine the classification results of several k-NN classifiers as final prediction. In our framework of IQ, we develop two time-series classification algorithms, IQ-MAX and IQ-WV. In our experiments on 35 commonly used benchmark data sets, we show that both IQ-MAX and IQ-WV outperform two baselines. © 2011 IEEE.

关键词： Time series

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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 goal is to predict student marks or how well will they perform in a given exercise. Since in many datasets the target variable is binary, i.e. a student has solved the exercise or failed in it, it would be natural to look at this problem as to a classification task. Another, probably not so usual case could be when we only have a so-called positive feedback, i.e. only the successful solutions are recorded. In this case, neither the regression nor the classification approaches would be useful and one could look on this problem as to a ranking task. We propose to look at solving the student performance prediction as a classification or ranking tasks, respectively, where models are optimized for appropriate error measures which are the Hinge loss and the Area under the ROC curve. Experimental comparison of these techniques are introduced using two, large-scale datasets. Both methods are well known in their respective fields, thus the goal of this paper is to introduce them in the educational data mining community.

关键词： Students

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