A Collaborative Training Algorithm for Distributed Learning
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[1] Robert Nowak,et al. Distributed optimization in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.
[2] Sanjeev R. Kulkarni,et al. A deterministic approach to throughput scaling in wireless networks , 2002, IEEE Transactions on Information Theory.
[3] Edward J. Coyle,et al. An energy efficient hierarchical clustering algorithm for wireless sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).
[4] Brendan J. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[5] H.-A. Loeliger,et al. An introduction to factor graphs , 2004, IEEE Signal Process. Mag..
[6] G. Wahba. Spline models for observational data , 1990 .
[7] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[8] M.G. Rabbat,et al. Generalized consensus computation in networked systems with erasure links , 2005, IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, 2005..
[9] G.B. Giannakis,et al. Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.
[10] R.L. Moses,et al. Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.
[11] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[12] Heinz H. Bauschke,et al. On Projection Algorithms for Solving Convex Feasibility Problems , 1996, SIAM Rev..
[13] H. Vincent Poor,et al. Regression in sensor networks: training distributively with alternating projections , 2005, SPIE Optics + Photonics.
[14] Robert D. Nowak,et al. Distributed EM algorithms for density estimation and clustering in sensor networks , 2003, IEEE Trans. Signal Process..
[15] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[16] Andreas F. Molisch,et al. Localization via Ultra- Wideband Radios , 2005 .
[17] L. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .
[18] Adam Krzyzak,et al. A Distribution-Free Theory of Nonparametric Regression , 2002, Springer series in statistics.
[19] Robert D. Nowak,et al. Quantized incremental algorithms for distributed optimization , 2005, IEEE Journal on Selected Areas in Communications.
[20] L. Williams,et al. Contents , 2020, Ophthalmology (Rochester, Minn.).
[21] Michael I. Jordan,et al. Nonparametric decentralized detection using kernel methods , 2005, IEEE Transactions on Signal Processing.
[22] Martin J. Wainwright,et al. Distributed fusion in sensor networks: a graphical models perspective , 2006 .
[23] Mung Chiang,et al. The value of clustering in distributed estimation for sensor networks , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.
[24] C. Guestrin,et al. Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.
[25] Dimitri P. Bertsekas,et al. Incremental Subgradient Methods for Nondifferentiable Optimization , 2001, SIAM J. Optim..
[26] V. Delouille,et al. Robust distributed estimation in sensor networks using the embedded polygons algorithm , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.
[27] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[28] Andrzej Stachurski,et al. Parallel Optimization: Theory, Algorithms and Applications , 2000, Parallel Distributed Comput. Pract..
[29] Panganamala Ramana Kumar,et al. Extended message passing algorithm for inference in loopy Gaussian graphical models , 2004, Ad Hoc Networks.
[30] Cynthia Rudin,et al. Stability Analysis for Regularized Least Squares Regression , 2005, ArXiv.
[31] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[32] H. Vincent Poor,et al. An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.
[33] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[34] H. Vincent Poor,et al. An introduction to signal detection and estimation (2nd ed.) , 1994 .
[35] Balázs Kégl,et al. Privacy-preserving boosting , 2007, Data Mining and Knowledge Discovery.
[36] Panganamala Ramana Kumar,et al. RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .
[37] Bruno Sinopoli,et al. A kernel-based learning approach to ad hoc sensor network localization , 2005, TOSN.
[38] Robert J. McEliece,et al. The generalized distributive law , 2000, IEEE Trans. Inf. Theory.
[39] D. Bertsekas,et al. Convergen e Rate of In remental Subgradient Algorithms , 2000 .
[40] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[41] Carlos Guestrin,et al. A robust architecture for distributed inference in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..
[42] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[43] Mark A. Paskin,et al. Junction tree algorithms for solving sparse linear systems , 2003 .
[44] Alexander J. Smola,et al. Learning with kernels , 1998 .
[45] Manfred K. Warmuth,et al. Additive versus exponentiated gradient updates for linear prediction , 1995, STOC '95.
[46] John N. Tsitsiklis,et al. Parallel and distributed computation , 1989 .
[47] Benjamin Van Roy,et al. Distributed Optimization in Adaptive Networks , 2003, NIPS.
[48] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[49] Urbashi Mitra,et al. Boundary Estimation in Sensor Networks: Theory and Methods , 2003, IPSN.
[50] H. Vincent Poor,et al. Consistency in models for distributed learning under communication constraints , 2005, IEEE Transactions on Information Theory.
[51] H. Vincent Poor,et al. Distributed Kernel Regression: An Algorithm for Training Collaboratively , 2006, 2006 IEEE Information Theory Workshop - ITW '06 Punta del Este.
[52] Zoran Obradovic,et al. The distributed boosting algorithm , 2001, KDD '01.
[53] Thomas Kailath,et al. RKHS approach to detection and estimation problems-I: Deterministic signals in Gaussian noise , 1971, IEEE Trans. Inf. Theory.