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My research activity focuses on the field of Machine Learning. Two key chal- lenges in most machine learning applications are uncertainty and complexity.
This research is based on the use of the vast plethora of techniques developed in the field of Logic Programming, in which the distribution semantics is one ...
The study compares the academic achievements for Hanyu Pinyin of two different groups of students whereby the experimental group adopts the web based ...
This inefficiency in parameter estimation will cause the learning algorithms to overfit and may result in biased struc- ture learning. One solution is to ...
Statistical relational learning aka probabilistic inductive logic programming deals with machine learning and data mining in relational domains.
• parameter learning. • structure learning: can use ordinary relational learning methods like FOIL or other ILP algorithms to learn new formula. Page 24. MLN ...
Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in.
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Markov logic networks (MLNs) are an expres- sive representation for statistical relational learn- ing that generalizes both first-order logic and.
Jun 28, 2016 · 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 ...
Structure learning where both L and λ have to be learned from the data. Below, we will sketch basic parameter estimation and structure learning tech- niques ...