@inproceedings{fages_machine_2006,
address = {Berlin, Heidelberg},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Machine {Learning} {Biochemical} {Networks} from {Temporal} {Logic} {Properties}},
isbn = {978-3-540-46236-1},
doi = {10/dd8},
abstract = {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.},
language = {en},
booktitle = {Transactions on {Computational} {Systems} {Biology} {VI}},
publisher = {Springer},
author = {Fages, François and Calzone, Laurence and Chabrier-Rivier, Nathalie and Soliman, Sylvain},
editor = {Priami, Corrado and Plotkin, Gordon},
year = {2006},
note = {ZSCC: NoCitationData[s0]},
keywords = {Abstract machines, Biology, Classical ML, Machine learning, Symbolic logic, Systems biology},
pages = {68--94}
}
@inproceedings{izbicki_algebraic_2013,
title = {Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training},
shorttitle = {Algebraic classifiers},
abstract = {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.},
booktitle = {{ICML}},
author = {Izbicki, Michael},
year = {2013},
note = {ZSCC: 0000013},
keywords = {Algebra, Categorical ML, Machine learning}
}
@misc{murfet_algebra_nodate,
title = {Algebra and {Artiﬁcial} {Intelligence}},
language = {en},
author = {Murfet, Daniel},
note = {ZSCC: NoCitationData[s0]},
keywords = {Algebra, Classical ML, Machine learning, Sketchy}
}
@misc{murfet_mathematics_nodate,
title = {Mathematics of {AlphaGo}},
author = {Murfet, Daniel},
note = {ZSCC: NoCitationData[s0]},
keywords = {Classical ML, Machine learning}
}
@misc{murfet_linear_nodate,
title = {Linear logic and deep learning},
language = {en},
author = {Murfet, Daniel and Hu, Huiyi},
note = {ZSCC: NoCitationData[s0]},
keywords = {Categorical ML, Linear logic, Machine learning, Semantics}
}