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The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention.
Human performance on the traveling salesman and related problems: A review
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A classification of formulations for the (time-dependent) traveling salesman problem
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An internal model for sensorimotor integration
Daniel M. Wolpert, Zoubin Ghahramani, and Michael I. Jordan · 1995
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Human performance on the traveling salesman problem
J. N. Macgregor and T. Ormerod · 1996
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Pierluigi Crescenzi, Deborah Goldman, Christos Papadimitriou, Antonio Piccolboni, and Mihalis Yannakakis · 1998
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The role of occam’s razor in knowledge discovery
Pedro Domingos · 1999
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Determining computational complexity from characteristic ‘phase transitions’
Rémi Monasson, Riccardo Zecchina, Scott Kirkpatrick, Bart Selman, and Lidror Troyansky · 1999
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Assert: A physician-in-the-loop content-based retrieval system for hrct image databases
Chi-Ren Shyu, Carla E. Brodley, Avinash C. Kak, Akio Kosaka, Alex M. Aisen, and Lynn S. Broderick · 1999
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Simulating human performance on the traveling salesman problem
Bradley J. Best and Herbert A. Simon · 2000
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Using traveling salesman problem algorithms for evolutionary tree construction
Chantal Korostensky and Gaston H Gonnet · 2000
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Ubiquitous healthcare: The onkonet mobile agents architecture
Stefan Kirn · 2002
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Computational complexity
Christos H. Papadimitriou · 2003
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Inferring causal networks from observations and interventions
Mark Steyvers, Joshua B. Tenenbaum, Eric-Jan Wagenmakers, and Ben Blum · 2003
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Convex hull and tour crossings in the euclidean traveling salesperson problem: Implications for human performance studies
Iris Van Rooij, Ulrike Stege, and Alissa Schactman · 2003
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Ant Colony Optimization
M Dorigo and T Stutzle · 2004
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The Bayesian brain: the role of uncertainty in neural coding and computation
David C Knill and Alexandre Pouget · 2004
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Ant colony optimization for job shop scheduling problem
Mario Ventresca and Beatrice M Ombuki · 2004
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Dynamic ant colony optimisation
Daniel Angus and Tim Hendtlass · 2005
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Improving ant systems using a local updating rule
Camelia-Mihaela Pintea and D Dumitrescu · 2005
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A multi-step approach to time series analysis and gene expression clustering
Roberto Amato, Angelo Ciaramella, N Deniskina, Carmine Del Mondo, Diego di Bernardo, Ciro Donalek, Giuseppe Longo, Giuseppe Mangano, Gennaro Miele, and Giancarlo Raiconi · 2006
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Introspective subgroup analysis for interactive knowledge refinement
Martin Atzmueller, Joachim Baumeister, and Frank Puppe · 2006
Mercer’s theorem on general domains: On the interaction between measures, kernels, and rkhss
Ingo Steinwart and Clint Scovel · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Human–-computer interaction & knowledge discovery (HCI-KDD): What is the benefit of bringing those two fields to work together?
Andreas Holzinger · 2013
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Optical character recognition in real environments using neural networks and k-nearest neighbor
Oliviu Matei, Petrica C Pop, and Honoroi Vălean · 2013
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The future of human-in-the-loop cyber-physical systems
Gunar Schirner, Deniz Erdogmus, Kaushik Chowdhury, and Taskin Padir · 2013
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Gaussian process kernels for pattern discovery and extrapolation
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Probabilistic models of cognition: Conceptual foundations
Nick Chater, Joshua B. Tenenbaum, and Alan Yuille · 2006
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A short history of the traveling salesman problem
Gilbert Laporte · 2006
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Large scale multiple kernel learning
Soeren Sonnenburg, Gunnar Raetsch, Christin Schaefer, and Bernhard Schoelkopf · 2006
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Theory-based bayesian models of inductive learning and reasoning
Joshua B Tenenbaum, Thomas L Griffiths, and Charles Kemp · 2006
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Clustering and visualization approaches for human cell cycle gene expression data analysis
Francesco Napolitano, Giancarlo Raiconi, Roberto Tagliaferri, Angelo Ciaramella, Antonino Staiano, and Gennaro Miele · 2007
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Modeling surgical tool selection patterns as a” traveling salesman problem” for optimizing a modular surgical tool system
Carl A Nelson, David J Miller, and Dmitry Oleynikov · 2007
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Andrew G. Wilson and Ryan P. Adams · 2013
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Power to the people: The role of humans in interactive machine learning
Saleema Amershi, Maya Cakmak, William B. Knox, and Todd Kulesza · 2014
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Support vector machines and evolutionary algorithms for classification
Catalin Stoean and Ruxandra Stoean · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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A rational model of function learning
Christopher G Lucas, Thomas L Griffiths, Joseph J Williams, and Michael L Kalish · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein · 2015
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas · 2015
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The human kernel
Andrew G. Wilson, Christoph Dann, Chris Lucas, and Eric P. Xing · 2015
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Wide and deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, and Mustafa Ispir · 2016
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Ant-based system analysis on the traveling salesman problem under real-world settings
Gloria Cerasela Crişan, Elena Nechita, and Vasile Palade · 2016
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Visual analytics for concept exploration in subspaces of patient groups: Making sense of complex datasets with the doctor-in-the-loop
Michael Hund, Dominic Boehm, Werner Sturm, Michael Sedlmair, Tobias Schreck, Torsten Ullrich, Daniel A. Keim, Ljiljana Majnaric, and Andreas Holzinger · 2016
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The right to be forgotten: Towards machine learning on perturbed knowledge bases
Bernd Malle, Peter Kieseberg, Edgar Weippl, and Andreas Holzinger · 2016
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Reasoning under uncertainty: Towards collaborative interactive machine learning
Sebastian Robert, Sebastian Büttner, Carsten Röcker, and Andreas Holzinger · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Emergency management using geographic information systems: application to the first romanian traveling salesman problem instance
Gloria Cerasela Crişan, Camelia-M Pintea, and Vasile Palade · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau, and Sebastian Thrun · 2017
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Do not disturb? classifier behavior on perturbed datasets. in: , pp. (in print), cham: Springer international
Bernd Malle, Peter Kieseberg, and Andreas Holzinger · 2017
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Online learning of symbolic concepts
Pratiksha Thaker, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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