Results 61 to 70 of about 836,133 (267)
srlearn: A Python Library for Gradient-Boosted Statistical Relational Models [PDF]
arXiv, 2019 We present srlearn, a Python library for boosted statistical relational models. We adapt the scikit-learn interface to this setting and provide examples for how this can be used to express learning and inference problems.
arxiv
Journal of the Association for Information Science and Technology, EarlyView.Abstract Using intersectionality as a critical theoretical framework and analytical tool, this study investigated the HIV/AIDS information practices of Black sexual minority men (SMM). Twenty‐two Black SMM were interviewed about their HIV/AIDS‐related information practices.
Megan Threats
wiley +1 more source
Self‐report measures of secure attachment in adulthood: A systematic review
Clinical Psychology &Psychotherapy, Volume 29, Issue 6, Page 1812-1842, November/December 2022., 2022 Abstract Background Secure attachment in adulthood is associated with many markers of adaptive functioning. Valid and reliable self‐report measures of attachment security could provide a practical tool to help advance strengths‐based research and clinical work.
Miranda Justo‐Núñez +2 more
wiley +1 more source
A Learning Algorithm for Relational Logistic Regression: Preliminary Results [PDF]
arXiv, 2016 Relational logistic regression (RLR) is a representation of conditional probability in terms of weighted formulae for modelling multi-relational data. In this paper, we develop a learning algorithm for RLR models. Learning an RLR model from data consists of two steps: 1- learning the set of formulae to be used in the model (a.k.a.
arxiv
A Probit Tensor Factorization Model For Relational Learning [PDF]
arXiv, 2021 With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational learning is link prediction, i.e., predicting whether certain relations exist in the knowledge graph. A large number of
arxiv
Clinical Psychology &Psychotherapy, Volume 29, Issue 6, Page 1942-1949, November/December 2022., 2022 Abstract Supervision seems to be an essential part of therapist training and thus also of implementing evidence‐based practices. However, there is a shortage of valid and reliable instruments for objective assessment of supervision competence that include both global measures and frequency counts of behaviour—two essential aspects of supervisory ...
Maria Beckman +4 more
wiley +1 more source
Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning [PDF]
Journal of Machine Learning Research, 24 (2023) 1-51, 2021 Comparing probability distributions is at the crux of many machine learning algorithms. Maximum Mean Discrepancies (MMD) and Wasserstein distances are two classes of distances between probability distributions that have attracted abundant attention in past years.
arxiv
MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
Applied Sciences, 2019 Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the ...
Cheol Young Park +1 more
doaj +1 more source
Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification [PDF]
arXiv, 2019 Automatically labeling multiple styles for every song is a comprehensive application in all kinds of music websites. Recently, some researches explore review-driven multi-label music style classification and exploit style correlations for this task. However, their methods focus on mining the statistical relations between different music styles and only
arxiv
Domain-Liftability of Relational Marginal Polytopes [PDF]
arXiv, 2020 We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models. Here, given some first-order logic formula, we can define its relational marginal statistic to be the fraction of groundings that make this formula true in a given ...
arxiv