Prediction of Students' Performance Based on the Hybrid IDA‐SVR Model
H Xu - Complexity, 2022 - Wiley Online Library
Students' performance is an important factor for the evaluation of teaching quality in
colleges. The aim of this study is to propose a novel intelligent approach to predict students' …
colleges. The aim of this study is to propose a novel intelligent approach to predict students' …
Fitted q-learning for relational domains
We consider the problem of Approximate Dynamic Programming in relational domains.
Inspired by the success of fitted Q-learning methods in propositional settings, we develop …
Inspired by the success of fitted Q-learning methods in propositional settings, we develop …
Combination prediction method of students' performance based on ant colony algorithm
H Xu, M Kim - Plos one, 2024 - journals.plos.org
Students' performance is an important factor for the evaluation of teaching quality in
colleges. The prediction and analysis of students' performance can guide students' learning …
colleges. The prediction and analysis of students' performance can guide students' learning …
Predicting crash injury severity in smart cities: a novel computational approach with wide and deep learning model
Smart cities came out as highly knowledgeable bio-networks, offering intelligent services
and innovative solutions to urban problems. With rapid development, urbanization, and …
and innovative solutions to urban problems. With rapid development, urbanization, and …
Scaling the weight parameters in Markov logic networks and relational logistic regression models
F Weitkämper - arXiv preprint arXiv:2103.15140, 2021 - arxiv.org
We consider Markov logic networks and relational logistic regression as two fundamental
representation formalisms in statistical relational artificial intelligence that use weighted …
representation formalisms in statistical relational artificial intelligence that use weighted …
Non-parametric learning of lifted restricted boltzmann machines
We consider the problem of discriminatively learning Restricted Boltzmann Machines in the
presence of relational data. Unlike previous approaches that employ a rule learner (for …
presence of relational data. Unlike previous approaches that employ a rule learner (for …
Probabilities of the third type: statistical relational learning and reasoning with relative frequencies
F Weitkämper - Journal of Artificial Intelligence Research, 2024 - jair.org
Dependencies on the relative frequency of a state in the domain are common when
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
Functional lifted Bayesian networks: statistical relational learning and reasoning with relative frequencies
F Weitkämper - International Conference on Inductive Logic …, 2022 - Springer
Dependencies on the relative frequency of a state in the domain are common when
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
[PDF][PDF] Statistical Relational Artificial Intelligence with Relative Frequencies: A Contribution to Modelling and Transfer Learning across Domain Sizes.
F Weitkämper - arXiv preprint arXiv:2202.10367, 2022 - scholar.archive.org
Dependencies on the relative frequency of a state in the domain are common when
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
modelling probabilistic dependencies on relational data. For instance, the likelihood of a …
Discriminative non-parametric learning of arithmetic circuits
Abstract Arithmetic Circuits (AC) and Sum-Product Networks (SPN) have recently gained
significant interest by virtue of being tractable deep probabilistic models. We propose the …
significant interest by virtue of being tractable deep probabilistic models. We propose the …