Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning in graphical models
The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
The Journal of Machine Learning Research
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Robust distributed estimation in sensor networks using the embedded polygons algorithm
Proceedings of the 3rd international symposium on Information processing in sensor networks
A kernel-based learning approach to ad hoc sensor network localization
ACM Transactions on Sensor Networks (TOSN)
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
Nonparametric decentralized detection using kernel methods
IEEE Transactions on Signal Processing
The generalized distributive law
IEEE Transactions on Information Theory
The capacity of wireless networks
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A deterministic approach to throughput scaling in wireless networks
IEEE Transactions on Information Theory
Consistency in models for distributed learning under communication constraints
IEEE Transactions on Information Theory
Quantized incremental algorithms for distributed optimization
IEEE Journal on Selected Areas in Communications
Consensus-Based Distributed Support Vector Machines
The Journal of Machine Learning Research
Decentralized sparse signal recovery for compressive sleeping wireless sensor networks
IEEE Transactions on Signal Processing
Distributed sparse linear regression
IEEE Transactions on Signal Processing
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Distributed parametric and nonparametric regression with on-line performance bounds computation
Automatica (Journal of IFAC)
| Hi-index | 754.84 |
In this paper, an algorithm is developed for collaboratively training networks of kernel-linear least-squares regression estimators. The algorithm is shown to distributively solve a relaxation of the classical centralized least-squares regression problem. A statistical analysis shows that the generalization error afforded agents by the collaborative training algorithm can be bounded in terms of the relationship between the network topology and the representational capacity of the relevant reproducing kernel Hilbert space. Numerical experiments suggest that the algorithm is effective at reducing noise. The algorithm is relevant to the problem of distributed learning in wireless sensor networks by virtue of its exploitation of local communication. Several new questions for statistical learning theory are proposed.