Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications
暂无分享,去创建一个
[1] Naveen Verma,et al. Ultralow-power electronics for biomedical applications. , 2008, Annual review of biomedical engineering.
[2] Sun-Yuan Kung,et al. Biometric Authentication: A Machine Learning Approach , 2004 .
[3] T. Kailath,et al. VLSI and Modern Signal Processing , 1984 .
[4] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[5] Sun-Yuan Kung,et al. Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios , 2010, J. Signal Process. Syst..
[6] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[7] Ali H. Shoeb,et al. Application of Machine Learning To Epileptic Seizure Detection , 2010, ICML.
[8] S. Kung,et al. VLSI Array processors , 1985, IEEE ASSP Magazine.
[9] Ali H. Shoeb,et al. Application of machine learning to epileptic seizure onset detection and treatment , 2009 .
[10] Naveen Verma,et al. A low-energy computation platform for data-driven biomedical monitoring algorithms , 2011, 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC).
[11] John Guttag,et al. Reducing energy consumption of multi-channel mobile medical monitoring algorithms , 2008, HealthNet '08.
[12] Naveen Verma,et al. A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System , 2010, IEEE Journal of Solid-State Circuits.
[13] A.-T. Avestruz,et al. A 2 $\mu\hbox{W}$ 100 nV/rtHz Chopper-Stabilized Instrumentation Amplifier for Chronic Measurement of Neural Field Potentials , 2007, IEEE Journal of Solid-State Circuits.
[14] Eric Dishman,et al. Inventing wellness systems for aging in place , 2004, Computer.
[15] Miguel Figueroa,et al. Competitive learning with floating-gate circuits , 2002, IEEE Trans. Neural Networks.
[16] Gregory Molnar,et al. Creating support circuits for the nervous system: Considerations for “brain-machine” interfacing , 2009, 2009 Symposium on VLSI Circuits.
[17] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[18] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[19] Pat Langley,et al. Editorial: On Machine Learning , 1986, Machine Learning.
[20] E.L. Glassman,et al. Reducing the Number of Channels for an Ambulatory Patient-Specific EEG-based Epileptic Seizure Detector by Applying Recursive Feature Elimination , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[21] Eric Panken,et al. A micropower support vector machine based seizure detection architecture for embedded medical devices , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[22] Philip de Chazal,et al. Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.
[23] Peter J.F. Lucas. Bayesian analysis, pattern analysis, and data mining in health care , 2004, Current opinion in critical care.
[24] Sun-Yuan Kung,et al. Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.
[25] David Tak-Wai Hau,et al. Learning Qualitative Models from Physiological Signals , 1994 .
[26] Refet Firat Yazicioglu,et al. A 60 $\mu$W 60 nV/$\surd$Hz Readout Front-End for Portable Biopotential Acquisition Systems , 2007, IEEE Journal of Solid-State Circuits.
[27] Naveen Verma,et al. Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection , 2011 .