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Machine Learning
Kluwer Academic Publishers
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Relational data factorization
Paramonov S., Leeuwen M., Raedt L. Machine Learning 106(12): 1867-1904, 2017. Type: Article
General methods take advantage of developing frameworks for data mining and machine learning that can be specialized for efficiency according to the problem domain. This paper discusses a declarative modeling method as a form of relati...
Feb 7 2019
Ultra-strong machine learning: comprehensibility of programs learned with ILP
Muggleton S., Schmid U., Zeller C., Tamaddoni-Nezhad A., Besold T. Machine Learning 107(7): 1119-1140, 2018. Type: Article
Recognizing the fact that “most of modern machine learning can be viewed as consistent with Michie’s weak criterion,” the authors of this paper are motivated to work on Michie’s ultra-strong crit...
Dec 31 2018
A scalable preference model for autonomous decision-making
Peters M., Saar-Tsechansky M., Ketter W., Williamson S., Groot P., Heskes T. Machine Learning 107(6): 1039-1068, 2018. Type: Article
In some consumer markets, prices are determined by the limited availability of goods and customers choose from a small set of options. The outcome is often determined by simple tradeoffs between the most critical attributes....
Oct 12 2018
Learning safe multi-label prediction for weakly labeled data
Wei T., Guo L., Li Y., Gao W. Machine Learning 107(4): 703-725, 2018. Type: Article
Many real-world applications involve learning in the presence of multiple labels. For example, in the case of images, a single image may be labeled sky, cloud, or even flower. To make matters more complicated, the dataset for training ...
Sep 11 2018
Learning deep kernels in the space of dot product polynomials
Donini M., Aiolli F. Machine Learning 106(9-10): 1245-1269, 2017. Type: Article
A very detailed and thorough description of a novel approach, this paper presents a well-motivated technical account....
May 16 2018
A Bayesian nonparametric model for multi-label learning
Xuan J., Lu J., Zhang G., Xu R., Luo X. Machine Learning 106(11): 1787-1815, 2017. Type: Article
Existing generative models for multilabel learning require that the number of topics be fixed in advance. This paper proposes a Bayesian nonparametric model that does not have this requirement....
May 14 2018
Scalable computational techniques for centrality metrics on temporally detailed social network
Gunturi V., Shekhar S., Joseph K., Carley K. Machine Learning 106(8): 1133-1169, 2017. Type: Article
To analyze social networks, where social interactions change over time, special graphs and methods are needed. In this paper, social networks are represented as temporally detailed (TD) networks; the aim is to compute centrality metric...
Feb 15 2018
An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music
Cancino-Chacón C., Gadermaier T., Widmer G., Grachten M. Machine Learning 106(6): 887-909, 2017. Type: Article
Along with an appreciation for the art of Western classical music, researchers are also passionate about the study of its expressive interpretation forms. They are interested in modeling the relationship between musical expression and ...
Nov 6 2017
High-probability minimax probability machines
Cousins S., Shawe-Taylor J. Machine Learning 106(6): 863-886, 2017. Type: Article
To address the challenges of minimizing the future misclassification rate of a predictor, Lanckriet et al. [1] proposed minimax probability machines (MPMs) based on the minimax approach to build binary classifiers, which minimize the u...
Oct 18 2017
Context-based unsupervised ensemble learning and feature ranking
Soltanmohammadi E., Naraghi-Pour M., van der Schaar M. Machine Learning 105(3): 459-485, 2016. Type: Article
An unsupervised ensemble learning and feature ranking method in which the combiner has no information about the expert’s performance, methods, and the data with which they operate is proposed in this paper. The method uses ba...
Apr 20 2017
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