FLOC 2018: FEDERATED LOGIC CONFERENCE 2018

This page contains an index consisting of author-provided keywords.

A
abstraction learning	Automatic clustering of a network protocol with weakly-supervised clustering
automata learning	Automatic clustering of a network protocol with weakly-supervised clustering
B
Bayesian learning	Learning Tree Distributions by Hidden Markov Models
C
clustering	Learning Several Languages from Labeled Strings: State Merging and Evolutionary Approaches
Congruence	Decision problems for Clark-congruential languages
Context-free Grammars	Decision problems for Clark-congruential languages
D
decidability	Decision problems for Clark-congruential languages
DNF	Toward Learning Boolean Functions from Positive Examples
E
Equivalence	Decision problems for Clark-congruential languages
F
false negative rate	Toward Learning Boolean Functions from Positive Examples
false positive rate	Toward Learning Boolean Functions from Positive Examples
G
grammatical inference	Learning Several Languages from Labeled Strings: State Merging and Evolutionary Approaches
Graph Weighted Models	Learning Graph Weighted Models on Pictures
H
hidden tree Markov model	Learning Tree Distributions by Hidden Markov Models
L
Learning from positive examples	Toward Learning Boolean Functions from Positive Examples
learning transductions	Learning Tree Distributions by Hidden Markov Models
Learning Weighted Automata	Learning Graph Weighted Models on Pictures
learning-based testing	Automatic clustering of a network protocol with weakly-supervised clustering
M
Minimally Adequate Teacher	Decision problems for Clark-congruential languages
multiple languages	Learning Several Languages from Labeled Strings: State Merging and Evolutionary Approaches
P
Picture Series	Learning Graph Weighted Models on Pictures
S
sparse Boolean functions	Toward Learning Boolean Functions from Positive Examples
state-merging	Learning Several Languages from Labeled Strings: State Merging and Evolutionary Approaches
Structured Data	Learning Graph Weighted Models on Pictures
T
tree automata	Learning Tree Distributions by Hidden Markov Models

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