Titlebook: Inductive Logic Programming; 24th International C Jesse Davis,Jan Ramon Conference proceedings 2015 Springer International Publishing Switz

高调

Pascal Welke,Tamás Horváth,Stefan Wrobelto assess the environmental impacts from its production. We focus on a case study involving the antibacterial gel from a company in Mexico. We completed the inventory analysis in collaboration with the company and compiled the impact assessments using the GaBi software and the ReCiPe method. The res

细胞学

救护车

心胸开阔

Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning, as few as two meta-rules are complete and sufficient for generating all hypotheses. In our experiments we compare the effect of using a minimal set of meta-rules to randomly chosen subsets of the maximal set of meta-rules. In general the minimal set of meta-rules leads to lower runtimes and higher

Amylase

Goal and Plan Recognition via Parse Trees Using Prefix and Infix Probability Computation, incomplete logs and empirically demonstrated the superiority of our approach compared to other approaches which do not use parsing. We also showed that it is possible to estimate the most likely plans from incomplete logs. All prefix and infix probability computation together with the computation o

SPECT

Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge,on model (the structure and parameters). We demonstrate on three domains that this form of weak supervision yields superior results when learning structure compared to using distant supervision labels or a smaller set of gold-standard labels.

停止偿付

Digest

Learning Prime Implicant Conditions from Interpretation Transition, general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.

AMPLE

0302-9743 ional machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.978-3-319-23707-7978-3-319-23708-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

抱负

Statistical Relational Learning for Handwriting Recognition,inally, a relational One-vs-All classifier is learned using relational functional gradient boosting (RFGB). We evaluate our approach on two datasets and demonstrate comparable accuracy to a well-established, meticulously engineered approach in the handwriting recognition paradigm.