53,157 research outputs found
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
Towards Automated Performance Bug Identification in Python
Context: Software performance is a critical non-functional requirement,
appearing in many fields such as mission critical applications, financial, and
real time systems. In this work we focused on early detection of performance
bugs; our software under study was a real time system used in the
advertisement/marketing domain.
Goal: Find a simple and easy to implement solution, predicting performance
bugs.
Method: We built several models using four machine learning methods, commonly
used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian
Networks, and Logistic Regression.
Results: Our empirical results show that a C4.5 model, using lines of code
changed, file's age and size as explanatory variables, can be used to predict
performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that
reducing the number of changes delivered on a commit, can decrease the chance
of performance bug injection.
Conclusions: We believe that our approach can help practitioners to eliminate
performance bugs early in the development cycle. Our results are also of
interest to theoreticians, establishing a link between functional bugs and
(non-functional) performance bugs, and explicitly showing that attributes used
for prediction of functional bugs can be used for prediction of performance
bugs
Learning with con gurable operators and RL-based heuristics
In this paper, we push forward the idea of machine learning
systems for which the operators can be modi ed and netuned for each
problem. This allows us to propose a learning paradigm where users can
write (or adapt) their operators, according to the problem, data representation
and the way the information should be navigated. To achieve
this goal, data instances, background knowledge, rules, programs and
operators are all written in the same functional language, Erlang. Since
changing operators a ect how the search space needs to be explored,
heuristics are learnt as a result of a decision process based on reinforcement
learning where each action is de ned as a choice of operator and
rule. As a result, the architecture can be seen as a `system for writing
machine learning systems' or to explore new operators.This work was supported by the MEC projects CONSOLIDER-INGENIO 26706 and
TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, and the REFRAME
project granted by the European Coordinated Research on Long-term Challenges in
Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA),
and funded by the Ministerio de Econom´ıa y Competitividad in Spain. Also, F.
Mart´ınez-Plumed is supported by FPI-ME grant BES-2011-045099MartĂnez Plumed, F.; Ferri RamĂrez, C.; Hernández Orallo, J.; RamĂrez Quintana, MJ. (2013). Learning with con gurable operators and RL-based heuristics. En New Frontiers in Mining Complex Patterns. Springer Verlag (Germany). 7765:1-16. https://doi.org/10.1007/978-3-642-37382-4_1S1167765Armstrong, J.: A history of erlang. In: Proceedings of the Third ACM SIGPLAN Conf. on History of Programming Languages, HOPL III, pp. 1–26. ACM (2007)Brazdil, P., Giraud-Carrier: Metalearning: Concepts and systems. In: Metalearning. Cognitive Technologies, pp. 1–10. Springer, Heidelberg (2009)DaumĂ© III, H., Langford, J.: Search-based structured prediction (2009)Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)Dietterich, T.G., Lathrop, R., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)DĹľeroski, S.: Towards a general framework for data mining. In: DĹľeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)Dzeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7–52 (2001), 10.1023/A:1007694015589Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer (2001)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Similarity functions for structured data. an application to decision trees. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 10(29), 109–121 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Web categorisation using distance-based decision trees. ENTCS 157(2), 35–40 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Bridging the Gap between Distance and Generalisation. Computational Intelligence (2012)Ferri-RamĂrez, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233–247. Springer, Heidelberg (2001)Gärtner, T.: Kernels for Structured Data. PhD thesis, Universitat Bonn (2005)Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: What is a learning classifier system? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 3–32. Springer, Heidelberg (2000)Holmes, J.H., Lanzi, P., Stolzmann, W.: Learning classifier systems: New models, successful applications. Information Processing Letters (2002)Kitzelmann, E.: Inductive programming: A survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010)Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)Lafferty, J., McCallum, A.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, pp. 282–289 (2001)Lloyd, J.W.: Knowledge representation, computation, and learning in higher-order logic (2001)Maes, F., Denoyer, L., Gallinari, P.: Structured prediction with reinforcement learning. Machine Learning Journal 77(2-3), 271–301 (2009)MartĂnez-Plumed, F., Estruch, V., Ferri, C., Hernández-Orallo, J., RamĂrez-Quintana, M.J.: Newton trees. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 174–183. Springer, Heidelberg (2010)Muggleton, S.: Inverse entailment and Progol. New Generation Computing (1995)Muggleton, S.H.: Inductive logic programming: Issues, results, and the challenge of learning language in logic. Artificial Intelligence 114(1-2), 283–296 (1999)Plotkin, G.: A note on inductive generalization. Machine Intelligence 5 (1970)Schmidhuber, J.: Optimal ordered problem solver. Maching Learning 54(3), 211–254 (2004)Srinivasan, A.: The Aleph Manual (2004)Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proc. of the Workshop on Relational Reinforcement Learning (2004)Tamaddoni-Nezhad, A., Muggleton, S.: A genetic algorithms approach to ILP. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 285–300. Springer, Heidelberg (2003)Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)Wallace, C.S., Dowe, D.L.: Refinements of MDL and MML coding. Comput. J. 42(4), 330–337 (1999)Watkins, C., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992
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