作者: 波動(dòng) 時(shí)間: 2025-3-21 23:16
978-3-030-61526-0Springer Nature Switzerland AG 2020作者: Intercept 時(shí)間: 2025-3-22 04:24
Discovery Science978-3-030-61527-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: colony 時(shí)間: 2025-3-22 07:39 作者: 發(fā)源 時(shí)間: 2025-3-22 12:34
Studienpionier:innen und Soziale Arbeitrtinent issue. This line of investigation is particularly lacking within clinical decision-making, for which the consequences can be life-altering. Certain real-world clinical ML decision tools are known to demonstrate significant levels of discrimination. There is currently indication that fairness作者: 痛苦一下 時(shí)間: 2025-3-22 15:53 作者: 痛苦一下 時(shí)間: 2025-3-22 18:58
https://doi.org/10.1007/978-3-7091-6825-7ting work is algorithm-specific, limited to simple together and apart constraints and does not attempt to satisfy all constraints. This limits applications including where satisfying all constraints is required such as fairness. In this work, we take the novel direction of post-processing the result作者: 難聽的聲音 時(shí)間: 2025-3-22 22:56
Bernd-Christian Funk,Bernd Schilcherbe extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional raw pixel space can be ineffective. To overcome these problems, we propose to use a deep convolutional embedding clustering framework. The model simultaneously optimizes t作者: invade 時(shí)間: 2025-3-23 03:27 作者: 材料等 時(shí)間: 2025-3-23 07:34 作者: 魯莽 時(shí)間: 2025-3-23 11:53
Claudia Hub,Verena Groer,Brigitte Wagenhalse of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between enti作者: Stagger 時(shí)間: 2025-3-23 17:06
Ralph Schneider,Theresa SchwarzkopfoExp, an OntoDM module which gives a more granular representation of a predictive modeling experiment and enables annotation of the experiment’s provenance, algorithm implementations, parameter settings and output metrics. This module is incorporated in SemanticHub, an online system that allows exec作者: 歡樂東方 時(shí)間: 2025-3-23 21:20 作者: analogous 時(shí)間: 2025-3-24 00:38
Positionierung und Internationalisierung,ew instances. In such dynamic environments, in which the underlying data distributions might evolve with time, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore作者: cancer 時(shí)間: 2025-3-24 03:01
Studierendenmarketing und Hochschulbranding-making. Most of the proposed fairness-aware learning algorithms process the data in offline settings and assume that the data is generated by a single concept without drift. Unfortunately, in many real-world applications, data is generated in a streaming fashion and can only be scanned once. In add作者: judiciousness 時(shí)間: 2025-3-24 10:07
Forschungsfrage(n) und Methodologie,m monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this . falls short in many real-world scenarios, where the true labels are not readily available to compute th作者: 友好 時(shí)間: 2025-3-24 14:44 作者: mediocrity 時(shí)間: 2025-3-24 16:00
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/281055.jpg作者: hair-bulb 時(shí)間: 2025-3-24 22:24
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approachy encountered in such settings and prevent a direct evaluation. First, the data may not have included all factors that affected past decisions. And second, past decisions may have led to unobserved outcomes. This is the case, for example, when a bank decides whether a customer should be granted a lo作者: 牽連 時(shí)間: 2025-3-25 01:21 作者: Antimicrobial 時(shí)間: 2025-3-25 05:07
: Combining Active Learning and Weak Supervisionearning (ML). One well-known method to gain labeled data efficiently is Active Learning (AL), where the learner interactively asks human experts to label the most informative data point. Nevertheless, even by applying AL in labeling tasks the amount of human effort is still too high and should be mi作者: bisphosphonate 時(shí)間: 2025-3-25 07:38 作者: LATER 時(shí)間: 2025-3-25 12:17 作者: Expiration 時(shí)間: 2025-3-25 18:02
Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require lab作者: GRATE 時(shí)間: 2025-3-25 20:36 作者: conquer 時(shí)間: 2025-3-26 02:47
COVID-19 Therapy Target Discovery with Context-Aware Literature Mininge of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between enti作者: Exposition 時(shí)間: 2025-3-26 07:09 作者: 燕麥 時(shí)間: 2025-3-26 10:44
Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schemacreasingly important. Consequently, nearly all community accepted guidelines and principles (e.g. FAIR and TRUST) for publishing such data in the digital ecosystem, stress the importance of semantic data enhancement. Having rich semantic annotation of DM datasets would support the data mining proces作者: irreducible 時(shí)間: 2025-3-26 16:32 作者: BARB 時(shí)間: 2025-3-26 17:35 作者: osteocytes 時(shí)間: 2025-3-27 00:42
Unsupervised Concept Drift Detection Using a Student–Teacher Approachm monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this . falls short in many real-world scenarios, where the true labels are not readily available to compute th作者: debacle 時(shí)間: 2025-3-27 01:14 作者: 最高點(diǎn) 時(shí)間: 2025-3-27 06:52
Conference proceedings 2020tions named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data..作者: MERIT 時(shí)間: 2025-3-27 10:53 作者: 蔓藤圖飾 時(shí)間: 2025-3-27 15:29
Bernd-Christian Funk,Bernd Schilcherualitative preliminary study on a collection of artworks made by Pablo Picasso shows the effectiveness of the model. The proposed method may assist in art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.作者: Chemotherapy 時(shí)間: 2025-3-27 20:55 作者: FECT 時(shí)間: 2025-3-27 23:33 作者: IOTA 時(shí)間: 2025-3-28 04:04
Iterative Multi-mode Discretization: Applications to Co-clusteringnd more accurate than state-of-the-art methods. We demonstrate the performance of IMMD-CC in comparison to several state-of-the-art methods on 100 data sets from a benchmark cohort, as well as 35 real-world datasets. The results show the promising potential of the proposed method.作者: 開始沒有 時(shí)間: 2025-3-28 06:49 作者: Intact 時(shí)間: 2025-3-28 14:03 作者: 漸強(qiáng) 時(shí)間: 2025-3-28 18:17
https://doi.org/10.1007/978-3-7091-6825-7the more recent approaches while being more computationally efficient. Finally, since all constraints are satisfied, our work can be applied to areas such as fairness including both group level and individual level fairness.作者: 連累 時(shí)間: 2025-3-28 19:36 作者: 托人看管 時(shí)間: 2025-3-29 01:18
Studierendenmarketing und Hochschulbranding is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.作者: GUISE 時(shí)間: 2025-3-29 07:01
https://doi.org/10.1007/978-3-658-32205-2rees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.作者: 榨取 時(shí)間: 2025-3-29 10:15 作者: DRAFT 時(shí)間: 2025-3-29 14:45
Constrained Clustering via Post-processingthe more recent approaches while being more computationally efficient. Finally, since all constraints are satisfied, our work can be applied to areas such as fairness including both group level and individual level fairness.作者: 閃光東本 時(shí)間: 2025-3-29 16:36 作者: propose 時(shí)間: 2025-3-29 23:12
FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.作者: Pillory 時(shí)間: 2025-3-30 00:24
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Featuresrees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.作者: Fermentation 時(shí)間: 2025-3-30 08:01 作者: 外表讀作 時(shí)間: 2025-3-30 09:20
Studienpionier:innen und Soziale Arbeitc processing, when applied to a real-world clinical ML decision algorithm which is known to discriminate with regards to racial characteristics. The results are promising, revealing that such techniques could significantly improve the fairness of clinical resource-allocation ML decision tools, parti作者: 射手座 時(shí)間: 2025-3-30 15:07
Mila Zeien,Judith L?bbert,Anna Zeienation, 55% of otherwise manually retrieved labels can be generated by WS techniques with a negligible loss of test accuracy by 0.31% only. To further prove the general applicability of our approach we applied it to six datasets from the AL challenge from Guyon et al., where over 90% of the labels co作者: 發(fā)起 時(shí)間: 2025-3-30 19:56 作者: 漂浮 時(shí)間: 2025-3-30 21:33
