TY - CONF
TI - Machine Learning Biochemical Networks from Temporal Logic Properties
AU - Fages, François
AU - Calzone, Laurence
AU - Chabrier-Rivier, Nathalie
AU - Soliman, Sylvain
A2 - Priami, Corrado
A2 - Plotkin, Gordon
T3 - Lecture Notes in Computer Science
AB - One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rule-based language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, concentration and population; and one temporal logic language used for formalizing the biological properties of the system. In this paper, we show how the temporal logic language can be turned into a specification language. We describe two algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. Then, with an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler.
C1 - Berlin, Heidelberg
C3 - Transactions on Computational Systems Biology VI
DA - 2006///
PY - 2006
DO - 10/dd8
DP - Springer Link
SP - 68
EP - 94
LA - en
PB - Springer
SN - 978-3-540-46236-1
KW - Abstract machines
KW - Biology
KW - Classical ML
KW - Machine learning
KW - Symbolic logic
KW - Systems biology
ER -
TY - CONF
TI - Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training
AU - Izbicki, Michael
AB - We use abstract algebra to derive new algorithms for fast cross-validation, online learning, and parallel learning. To use these algorithms on a classification model, we must show that the model has appropriate algebraic structure. It is easy to give algebraic structure to some models, and we do this explicitly for Bayesian classifiers and a novel variation of decision stumps called HomStumps. But not all classifiers have an obvious structure, so we introduce the Free HomTrainer. This can be used to give a "generic" algebraic structure to any classifier. We use the Free HomTrainer to give algebraic structure to bagging and boosting. In so doing, we derive novel online and parallel algorithms, and present the first fast cross-validation schemes for these classifiers.
C3 - ICML
DA - 2013///
PY - 2013
DP - Semantic Scholar
ST - Algebraic classifiers
KW - Algebra
KW - Categorical ML
KW - Machine learning
ER -
TY - SLIDE
TI - Algebra and Artiﬁcial Intelligence
A2 - Murfet, Daniel
LA - en
KW - Algebra
KW - Classical ML
KW - Machine learning
KW - Sketchy
ER -
TY - SLIDE
TI - Mathematics of AlphaGo
A2 - Murfet, Daniel
KW - Classical ML
KW - Machine learning
ER -
TY - SLIDE
TI - Linear logic and deep learning
A2 - Murfet, Daniel
A2 - Hu, Huiyi
LA - en
KW - Categorical ML
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -