Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency
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Davide Anguita | Luca Oneto | Sandro Ridella | Alessandro Ghio | S. Ridella | L. Oneto | D. Anguita | A. Ghio
[1] Davide Anguita,et al. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.
[2] Davide Anguita,et al. A Learning Machine with a Bit-Based Hypothesis Space , 2013, ESANN.
[3] Ambuj Tewari,et al. Smoothness, Low Noise and Fast Rates , 2010, NIPS.
[4] Gábor Lugosi,et al. Introduction to Statistical Learning Theory , 2004, Advanced Lectures on Machine Learning.
[5] Ingo Steinwart,et al. Fast rates for support vector machines using Gaussian kernels , 2007, 0708.1838.
[6] Davide Anguita,et al. The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers , 2011, NIPS.
[7] Peter L. Bartlett,et al. Localized Rademacher Complexities , 2002, COLT.
[8] Manfred Mücke,et al. Effects of Reduced Precision on Floating-Point SVM Classification Accuracy , 2011, International Conference on Conceptual Structures.
[9] Michael G. Epitropakis,et al. Hardware-friendly Higher-Order Neural Network Training using Distributed Evolutionary Algorithms , 2010, Appl. Soft Comput..
[10] Carlo Vercellis,et al. Discrete support vector decision trees via tabu search , 2004, Comput. Stat. Data Anal..
[11] V. Koltchinskii. Local Rademacher complexities and oracle inequalities in risk minimization , 2006, 0708.0083.
[12] Davide Anguita,et al. Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic , 2013, J. Univers. Comput. Sci..
[13] Andrew T. Campbell,et al. Bewell: A smartphone application to monitor, model and promote wellbeing , 2011, PervasiveHealth 2011.
[14] Davide Anguita,et al. In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine , 2012, Neural Processing Letters.
[15] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[16] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[17] A. Tsybakov,et al. Fast learning rates for plug-in classifiers , 2007, 0708.2321.
[18] Colin McDiarmid,et al. Surveys in Combinatorics, 1989: On the method of bounded differences , 1989 .
[19] Davide Anguita,et al. A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[20] L. Valiant. Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World , 2013 .
[21] Lawrence O. Hall,et al. Bit reduction support vector machine , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[22] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[23] Davide Anguita,et al. A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.
[24] P. Massart,et al. Discussion: Local Rademacher complexities and oracle inequalities in risk minimization , 2006 .
[25] Lorenzo Rosasco,et al. Learning from Examples as an Inverse Problem , 2005, J. Mach. Learn. Res..
[26] Raquel Valdés-Cristerna,et al. An FPGA Implementation of Linear Kernel Support Vector Machines , 2006, 2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006).
[27] B. Venkataramani,et al. FPGA Implementation of Support Vector Machine Based Isolated Digit Recognition System , 2009, 2009 22nd International Conference on VLSI Design.
[28] Sang-Woong Lee,et al. Real-Time Implementation of Face Recognition Algorithms on DSP Chip , 2003, AVBPA.
[29] Gert Cauwenberghs,et al. Kerneltron: Support Vector 'Machine' in Silicon , 2002, SVM.
[30] Enrique Alba,et al. Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[31] John Shawe-Taylor,et al. Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.
[32] Davide Anguita,et al. Selecting the hypothesis space for improving the generalization ability of Support Vector Machines , 2011, The 2011 International Joint Conference on Neural Networks.
[33] Diane J. Cook,et al. Pervasive computing at scale: Transforming the state of the art , 2012, Pervasive Mob. Comput..
[34] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[35] M. Talagrand. The Glivenko-Cantelli Problem , 1987 .
[36] Davide Anguita,et al. A support vector machine with integer parameters , 2008, Neurocomputing.
[37] P. Bartlett,et al. Local Rademacher complexities , 2005, math/0508275.
[38] Eliathamby Ambikairajah,et al. Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. , 2006, Physiological measurement.
[39] V. Koltchinskii,et al. Oracle inequalities in empirical risk minimization and sparse recovery problems , 2011 .
[40] Narayanan Vijaykrishnan,et al. A Hardware Efficient Support Vector Machine Architecture for FPGA , 2008, 2008 16th International Symposium on Field-Programmable Custom Computing Machines.