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Relevant bibliographies by topics / Classifier systems

Academic literature on the topic 'Classifier systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Classifier systems"

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ISHIBUCHI, Hisao. "Fuzzy Classifier Systems." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 4 (1998): 613–25. http://dx.doi.org/10.3156/jfuzzy.10.4_33.

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Geyer-Schulz, Andreas. "Holland classifier systems." ACM SIGAPL APL Quote Quad 25, no. 4 (June 8, 1995): 43–55. http://dx.doi.org/10.1145/206944.206955.

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Bull, Larry, Pler Luca Lanzi, and Wolfgang Stolzmann. "Learning Classifier Systems." Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, no. 3-4 (June 1, 2002): 143. http://dx.doi.org/10.1007/s005000100110.

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Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.

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Amerineni, Rajesh, Resh S. Gupta, and Lalit Gupta. "Multimodal Object Classification Models Inspired by Multisensory Integration in the Brain." Brain Sciences 9, no. 1 (January 2, 2019): 3. http://dx.doi.org/10.3390/brainsci9010003.

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Two multimodal classification models aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli are introduced. The feature-integrating model, inspired by multisensory integration in the subcortical superior colliculus, combines unimodal features which are subsequently classified by a multimodal classifier. The decision-integrating model, inspired by integration in primary cortical areas, classifies unimodal stimuli independently using unimodal classifiers and classifies the combined decisions using a multimodal classifier. The multimodal classifier models are implemented using multilayer perceptrons and multivariate statistical classifiers. Experiments involving the classification of noisy and attenuated auditory and visual representations of ten digits are designed to demonstrate the properties of the multimodal classifiers and to compare the performances of multimodal and unimodal classifiers. The experimental results show that the multimodal classification systems exhibit an important aspect of the “inverse effectiveness principle” by yielding significantly higher classification accuracies when compared with those of the unimodal classifiers. Furthermore, the flexibility offered by the generalized models enables the simulations and evaluations of various combinations of multimodal stimuli and classifiers under varying uncertainty conditions.

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Kovacs, T. "Learning classifier systems resources." Soft Computing - A Fusion of Foundations, Methodologies and Applications 6, no. 3-4 (June 1, 2002): 240–43. http://dx.doi.org/10.1007/s005000100119.

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Tomlinson, Andy, and Larry Bull. "Symbiogenesis in Learning Classifier Systems." Artificial Life 7, no. 1 (January 2001): 33–61. http://dx.doi.org/10.1162/106454601300328016.

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Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Symbiogenesis is the name given to the process by which symbiotic partners combine and unify, that is, become genetically linked, giving rise to new morphologies and physiologies evolutionarily more advanced than their constituents. The importance of this process in the evolution of complexity is now well established. Learning classifier systems are a machine learning technique that uses both evolutionary computing techniques and reinforcement learning to develop a population of cooperative rules to solve a given task. In this article we examine the use of symbiogenesis within the classifier system rule base to improve their performance. Results show that incorporating simple rule linkage does not give any benefits. The concept of (temporal) encapsulation is then added to the symbiotic rules and shown to improve performance in ambiguous/non-Markov environments.

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Croft, William. "Semantic universals in classifier systems." WORD 45, no. 2 (August 1994): 145–71. http://dx.doi.org/10.1080/00437956.1994.11435922.

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Mitlöhner, Johann. "Classifier systems and economic modeling." ACM SIGAPL APL Quote Quad 26, no. 4 (June 15, 1996): 77–86. http://dx.doi.org/10.1145/253417.253396.

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Dam, H. H., H. A. Abbass, C. Lokan, and Xin Yao. "Neural-Based Learning Classifier Systems." IEEE Transactions on Knowledge and Data Engineering 20, no. 1 (January 2008): 26–39. http://dx.doi.org/10.1109/tkde.2007.190671.

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Dissertations / Theses on the topic "Classifier systems"

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Tomlinson, Andrew Stephen. "Corporate classifier systems." Thesis, University of the West of England, Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300920.

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Joo, Hyonam. "Binary tree classifier and context classifier." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.

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Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption.
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Alkoot, Fuad M. "Design of multiple classifier systems." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/2264/.

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Hurst, Jacob Machar. "Learning classifier systems in robotic environments." Thesis, University of the West of England, Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274088.

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Ball, N. R. "Cognitive maps in Learning Classifier Systems." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280670.

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Roberts, Gary Allen. "Classifier systems for situated autonomous learning." Thesis, University of Edinburgh, 1991. http://hdl.handle.net/1842/20146.

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The ability to learn from experience is a key aspect of intelligence. Incorporating this ability into a computer is a formidable problem. Genetic algorithms coupled to learning classifier systems are powerful tools for tackling this task. While genetic algorithms can be shown to be near optimal solutions for the search task they perform, no similar proof exists for classifier systems. My research investigated two aspects of classifier systems, classifier selection and credit assignment. Explicit world models, look ahead and incremental planning are incorporated into the classifier system framework in order to make use of more of the information available to the system, and a more sophisticated approach to credit assignment is attempted. The investigation involved the construction of four different classifier systems, and testing each of these systems in three separate virtual worlds. Wilson's Animat research was carefully reconstructed, and used as the control in a scientific experiment testing the efficacy of the various strategies embodied in three experimental systems. The three experimental classifier systems all contained explicit world models and lookahead. One was an extension of Wilson's Animat, the other two involved an entirely new credit assignment scheme inspired by Watkins's Q-learning technique. Use of this technique enabled the incorporation of an incremental planner, similar to Sutton's Dyna-Q research, into one of the classifier systems, distinguishing it from the other Q-learning based classifier system. The research shows that use of explicit world models and lookahead significantly decreases the time required in order to discoverr paths to well rewarded goals. It also shows that incremental planning can be used to further increase learning speed. While the experimental classifier systems were quick at discovery, they did not necessarily exploit these discoveries. Because of this, the performance of these systems was variable between virtual worlds. The experimental systems were outperformed by the control in two out of the three virtual worlds investigated. Nevertheless, the value of explicit world models and lookahead within classifier systems is established. Likewise, the adaptation of Q-learning and incremental planning to classifier systems has been successfully demonstrated.

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Chou, Yu-Yu. "Hierarchical multiple classifier learning system /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/6042.

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Thiel, Christian [Verfasser]. "Multiple Classifier Systems Incorporating Uncertainty / Christian Thiel." München : Verlag Dr. Hut, 2010. http://d-nb.info/1009095625/34.

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Sancho, Asensio Andreu. "Facing online challenges using learning classifier systems." Doctoral thesis, Universitat Ramon Llull, 2014. http://hdl.handle.net/10803/144508.

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Els grans avenços en el camp de l’aprenentatge automàtic han resultat en el disseny de màquines competents que són capaces d’aprendre i d’extreure informació útil i original de l’experiència. Recentment, algunes d’aquestes tècniques d’aprenentatge s’han aplicat amb èxit per resoldre problemes del món real en àmbits tecnològics, mèdics, científics i industrials, els quals no es podien tractar amb tècniques convencionals d’anàlisi ja sigui per la seva complexitat o pel gran volum de dades a processar. Donat aquest èxit inicial, actualment els sistemes d’aprenentatge s’enfronten a problemes de complexitat més elevada, el que ha resultat en un augment de l’activitat investigadora entorn sistemes capaços d’afrontar nous problemes del món real eficientment i de manera escalable. Una de les famílies d’algorismes més prometedores en l’aprenentatge automàtic són els sistemes classificadors basats en algorismes genetics (LCSs), el funcionament dels quals s’inspira en la natura. Els LCSs intenten representar les polítiques d’actuació d’experts humans amb un conjunt de regles que s’empren per escollir les millors accions a realitzar en tot moment. Així doncs, aquests sistemes aprenen polítiques d’actuació de manera incremental a mida que van adquirint experiència a través de la informació nova que se’ls va presentant durant el temps. Els LCSs s’han aplicat, amb èxit, a camps tan diversos com la predicció de càncer de pròstata o el suport a la inversió en borsa, entre altres. A més en alguns casos s’ha demostrat que els LCSs realitzen tasques superant la precisió dels éssers humans. El propòsit d’aquesta tesi és explorar la naturalesa de l’aprenentatge online dels LCSs d’estil Michigan per a la mineria de grans quantitats de dades en forma de fluxos d’informació continus a alta velocitat i canviants en el temps. Molt sovint, l’extracció de coneixement a partir d’aquestes fonts de dades és clau per tal d’obtenir una millor comprensió dels processos que les dades estan descrivint. Així, aprendre d’aquestes dades planteja nous reptes a les tècniques tradicionals d’aprenentatge automàtic, les quals no estan dissenyades per tractar fluxos de dades continus i on els conceptes i els nivells de soroll poden variar amb el temps de forma arbitrària. La contribució de la present tesi pren l’eXtended Classifier System (XCS), el LCS d’estil Michigan més estudiat i un dels algoritmes d’aprenentatge automàtic més competents, com el punt de partida. D’aquesta manera els reptes abordats en aquesta tesi són dos: el primer desafiament és la construcció d’un sistema supervisat competent sobre el framework dels LCSs d’estil Michigan que aprèn dels fluxos de dades amb una capacitat de reacció ràpida als canvis de concepte i entrades amb soroll. Com moltes aplicacions científiques i industrials generen grans quantitats de dades sense etiquetar, el segon repte és aplicar les lliçons apreses per continuar amb el disseny de LCSs d’estil Michigan capaços de solucionar problemes online sense assumir una estructura a priori en els dades d’entrada.
Los grandes avances en el campo del aprendizaje automático han resultado en el diseño de máquinas capaces de aprender y de extraer información útil y original de la experiencia. Recientemente alguna de estas técnicas de aprendizaje se han aplicado con éxito para resolver problemas del mundo real en ámbitos tecnológicos, médicos, científicos e industriales, los cuales no se podían tratar con técnicas convencionales de análisis ya sea por su complejidad o por el gran volumen de datos a procesar. Dado este éxito inicial, los sistemas de aprendizaje automático se enfrentan actualmente a problemas de complejidad cada vez m ́as elevada, lo que ha resultado en un aumento de la actividad investigadora en sistemas capaces de afrontar nuevos problemas del mundo real de manera eficiente y escalable. Una de las familias más prometedoras dentro del aprendizaje automático son los sistemas clasificadores basados en algoritmos genéticos (LCSs), el funcionamiento de los cuales se inspira en la naturaleza. Los LCSs intentan representar las políticas de actuación de expertos humanos usando conjuntos de reglas que se emplean para escoger las mejores acciones a realizar en todo momento. Así pues estos sistemas aprenden políticas de actuación de manera incremental mientras van adquiriendo experiencia a través de la nueva información que se les va presentando. Los LCSs se han aplicado con éxito en campos tan diversos como en la predicción de cáncer de próstata o en sistemas de soporte de bolsa, entre otros. Además en algunos casos se ha demostrado que los LCSs realizan tareas superando la precisión de expertos humanos. El propósito de la presente tesis es explorar la naturaleza online del aprendizaje empleado por los LCSs de estilo Michigan para la minería de grandes cantidades de datos en forma de flujos continuos de información a alta velocidad y cambiantes en el tiempo. La extracción del conocimiento a partir de estas fuentes de datos es clave para obtener una mejor comprensión de los procesos que se describen. Así, aprender de estos datos plantea nuevos retos a las técnicas tradicionales, las cuales no están diseñadas para tratar flujos de datos continuos y donde los conceptos y los niveles de ruido pueden variar en el tiempo de forma arbitraria. La contribución del la presente tesis toma el eXtended Classifier System (XCS), el LCS de tipo Michigan más estudiado y uno de los sistemas de aprendizaje automático más competentes, como punto de partida. De esta forma los retos abordados en esta tesis son dos: el primer desafío es la construcción de un sistema supervisado competente sobre el framework de los LCSs de estilo Michigan que aprende de flujos de datos con una capacidad de reacción rápida a los cambios de concepto y al ruido. Como muchas aplicaciones científicas e industriales generan grandes volúmenes de datos sin etiquetar, el segundo reto es aplicar las lecciones aprendidas para continuar con el diseño de nuevos LCSs de tipo Michigan capaces de solucionar problemas online sin asumir una estructura a priori en los datos de entrada.
Last advances in machine learning have fostered the design of competent algorithms that are able to learn and extract novel and useful information from data. Recently, some of these techniques have been successfully applied to solve real-‐world problems in distinct technological, scientific and industrial areas; problems that were not possible to handle by the traditional engineering methodology of analysis either for their inherent complexity or by the huge volumes of data involved. Due to the initial success of these pioneers, current machine learning systems are facing problems with higher difficulties that hamper the learning process of such algorithms, promoting the interest of practitioners for designing systems that are able to scalably and efficiently tackle real-‐world problems. One of the most appealing machine learning paradigms are Learning Classifier Systems (LCSs), and more specifically Michigan-‐style LCSs, an open framework that combines an apportionment of credit mechanism with a knowledge discovery technique inspired by biological processes to evolve their internal knowledge. In this regard, LCSs mimic human experts by making use of rule lists to choose the best action to a given problem situation, acquiring their knowledge through the experience. LCSs have been applied with relative success to a wide set of real-‐ world problems such as cancer prediction or business support systems, among many others. Furthermore, on some of these areas LCSs have demonstrated learning capacities that exceed those of human experts for that particular task. The purpose of this thesis is to explore the online learning nature of Michigan-‐style LCSs for mining large amounts of data in the form of continuous, high speed and time-‐changing streams of information. Most often, extracting knowledge from these data is key, in order to gain a better understanding of the processes that the data are describing. Learning from these data poses new challenges to traditional machine learning techniques, which are not typically designed to deal with data in which concepts and noise levels may vary over time. The contribution of this thesis takes the extended classifier system (XCS), the most studied Michigan-‐style LCS and one of the most competent machine learning algorithms, as the starting point. Thus, the challenges addressed in this thesis are twofold: the first challenge is building a competent supervised system based on the guidance of Michigan-‐style LCSs that learns from data streams with a fast reaction capacity to changes in concept and noisy inputs. As many scientific and industrial applications generate vast amounts of unlabelled data, the second challenge is to apply the lessons learned in the previous issue to continue with the design of unsupervised Michigan-‐style LCSs that handle online problems without assuming any a priori structure in input data.

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Howard, Gerard David. "Constructivist and spiking neural learning classifier systems." Thesis, University of the West of England, Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573442.

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This thesis investigates the use of self-adaptation and neural constructivism within a neural Learning Classifier System framework. The system uses a classifier structure whereby each classifier condition is represented by an artificial neural network, which is used to compute an action in response to an environmental stimulus. We implement this neural representation in two modem Learning Classifier Systems, XCS and XCSF. A classic problem in neural networks revolves around network topology considerations; how many neurons should the network consist of? How should we configure their topological arrangement and inter-neural connectivity patterns to ensure high performance? Similarly in Learning Classifier Systems, hand-tuning of parameters is sometimes necessary to achieve acceptable system performance. We employ a number of mechanisms to address these potential deficiencies. Neural Constructivism is utilised to automatically alter network topology to reflect the complexity of the environment. It is shown that appropriate internal classifier complexity emerges during learning at a rate controlled by the learner. The resulting systems are applied to real-valued, noisy simulated maze environments and a simulated robotics platform. The main areas of novelty include the first use of self-adaptive constructivism within XCSF, the first implementation of temporally-sensitive spiking classifier representations within this constructive XC SF, and the demonstration of temporal functionality of such representations in noisy continuous-valued and robotic environments.

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Books on the topic "Classifier systems"

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Zhou, Zhi-Hua, Fabio Roli, and Josef Kittler, eds. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9.

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Roli, Fabio, and Josef Kittler, eds. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4.

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Bacardit, Jaume, Ester Bernadó-Mansilla, Martin V. Butz, Tim Kovacs, Xavier Llorà, and Keiki Takadama, eds. Learning Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88138-4.

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Bacardit, Jaume, Will Browne, Jan Drugowitsch, Ester Bernadó-Mansilla, and Martin V. Butz, eds. Learning Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17508-4.

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Schwenker, Friedhelm, Fabio Roli, and Josef Kittler, eds. Multiple Classifier Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20248-8.

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Lanzi, Pier Luca, Wolfgang Stolzmann, and Stewart W. Wilson, eds. Learning Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45027-0.

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Sansone, Carlo, Josef Kittler, and Fabio Roli, eds. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21557-5.

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Roli, Fabio, Josef Kittler, and Terry Windeatt, eds. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98227.

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Benediktsson, Jón Atli, Josef Kittler, and Fabio Roli, eds. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2.

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Lanzi, Pier Luca, Wolfgang Stolzmann, and Stewart W. Wilson, eds. Learning Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b94229.

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Book chapters on the topic "Classifier systems"

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Lanzi, Pier Luca. "Classifier Systems." In Encyclopedia of Machine Learning and Data Mining, 217–24. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_941.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Classifier Systems." In Encyclopedia of Machine Learning, 172–78. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_115.

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Hand, David J., Niall M. Adams, and Mark G. Kelly. "Multiple Classifier Systems Based on Interpretable Linear Classifiers." In Multiple Classifier Systems, 136–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_14.

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Bonissone, Piero, Neil Eklund, and Kai Goebel. "Using an Ensemble of Classifiers to Audit a Production Classifier." In Multiple Classifier Systems, 376–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_38.

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Lee, Wan-Jui, Robert P. W. Duin, and Horst Bunke. "Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems." In Multiple Classifier Systems, 155–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_16.

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Giacinto, Giorgio, and Fabio Roli. "Dynamic Classifier Selection." In Multiple Classifier Systems, 177–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_17.

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Eiben, A. E., and J. E. Smith. "Learning Classifier Systems." In Natural Computing Series, 115–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05094-1_7.

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Roli, Fabio. "Multiple Classifier Systems." In Encyclopedia of Biometrics, 981–86. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_148.

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Roli, Fabio. "Multiple Classifier Systems." In Encyclopedia of Biometrics, 1142–47. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_148.

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Geyer-Schulz, Andreas. "Fuzzy Classifier Systems." In Fuzzy Logic, 345–54. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_32.

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Conference papers on the topic "Classifier systems"

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Butz, Martin V. "Learning classifier systems." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389059.

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Lanzi, Pier Luca. "Learning classifier systems." In the 11th annual conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570406.

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Butz, Martin V. "Learning classifier systems." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002121.

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Geyer-Schulz, Andreas. "Holland classifier systems." In the international conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/206913.206955.

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Browne, Will N., and Ryan Urbanowicz. "Learning classifier systems." In Proceeding of the fifteenth annual conference companion. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2483909.

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Butz, Martin V. "Learning classifier systems." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274104.

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Butz, Martin V. "Learning classifier systems." In the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830898.

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Lanzi, Pier Luca. "Learning classifier systems." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2605343.

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Stein, Anthony. "Learning classifier systems." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3323393.

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Stein, Anthony, and Masaya Nakata. "Learning classifier systems." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389860.

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Reports on the topic "Classifier systems"

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Foor, Wesley E., Mark A. Getbehead, and James B. Rosetti. Adaptive Optical Neural Network Classifier Systems. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada335118.

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Backlund, Peter B., and John P. Eddy. Classifier-Guided Sampling for Complex Energy System Optimization. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1221709.

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Gaines, David A. Assessing the Cognitive Abilities of Alternate Learning Classifier System Architectures. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416405.

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Smith, Stella. Review of Combat Ammunition System (CAS) Classified Data Handling. Fort Belvoir, VA: Defense Technical Information Center, November 1996. http://dx.doi.org/10.21236/ada319830.

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Wurtz, R., and A. Kaplan. Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1236750.

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B. Gardiner, L.Graton, J.Longo, Jr T.Marks, B.Martinez, R. Strittmatter, C.Woods, and J. Joshua. Sixty Percent Conceptual Design Report: Enterprise Accountability System for Classified Removable Electronic Media. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/812106.

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O`Callaghan, P. B., R. A. Nelson, and A. J. Grambihler. Classified computer configuration control system (C{sup 4}S), revision 3, user`s information. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10161067.

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Caley, Jeffrey. A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2000.

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O`Callaghan, P. B., R. A. Nelson, and A. J. Grambihler. Classified Computer Configuration Control System (C{sup 4}S), Revision 3, Database Administrator`s Guide. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10160934.

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Staid, Andrea, and Christopher G. Valicka. Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1567836.

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