2,532 research outputs found
EigenGP: Gaussian Process Models with Adaptive Eigenfunctions
Gaussian processes (GPs) provide a nonparametric representation of functions.
However, classical GP inference suffers from high computational cost for big
data. In this paper, we propose a new Bayesian approach, EigenGP, that learns
both basis dictionary elements--eigenfunctions of a GP prior--and prior
precisions in a sparse finite model. It is well known that, among all
orthogonal basis functions, eigenfunctions can provide the most compact
representation. Unlike other sparse Bayesian finite models where the basis
function has a fixed form, our eigenfunctions live in a reproducing kernel
Hilbert space as a finite linear combination of kernel functions. We learn the
dictionary elements--eigenfunctions--and the prior precisions over these
elements as well as all the other hyperparameters from data by maximizing the
model marginal likelihood. We explore computational linear algebra to simplify
the gradient computation significantly. Our experimental results demonstrate
improved predictive performance of EigenGP over alternative sparse GP methods
as well as relevance vector machine.Comment: Accepted by IJCAI 201
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where the
majority of connections are fixed, either in a stochastic or a deterministic
fashion. Typical examples of such systems consist of multi-layered neural
network architectures where the connections to the hidden layer(s) are left
untrained after initialization. Limiting the training algorithms to operate on
a reduced set of weights inherently characterizes the class of Randomized
Neural Networks with a number of intriguing features. Among them, the extreme
efficiency of the resulting learning processes is undoubtedly a striking
advantage with respect to fully trained architectures. Besides, despite the
involved simplifications, randomized neural systems possess remarkable
properties both in practice, achieving state-of-the-art results in multiple
domains, and theoretically, allowing to analyze intrinsic properties of neural
architectures (e.g. before training of the hidden layers' connections). In
recent years, the study of Randomized Neural Networks has been extended towards
deep architectures, opening new research directions to the design of effective
yet extremely efficient deep learning models in vectorial as well as in more
complex data domains. This chapter surveys all the major aspects regarding the
design and analysis of Randomized Neural Networks, and some of the key results
with respect to their approximation capabilities. In particular, we first
introduce the fundamentals of randomized neural models in the context of
feed-forward networks (i.e., Random Vector Functional Link and equivalent
models) and convolutional filters, before moving to the case of recurrent
systems (i.e., Reservoir Computing networks). For both, we focus specifically
on recent results in the domain of deep randomized systems, and (for recurrent
models) their application to structured domains
A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses
This paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First, the initial dataset of available training responses is compressed via principal component analysis (PCA). Then, the compressed dataset is used to train compact surrogate models for the reduced PCA variables using advanced techniques for uncertainty quantification and parametric macromodeling. Specifically, in this work sparse polynomial chaos expansion and least-square support-vector machine regression are used, although the proposed methodology is general and applicable to any surrogate modeling strategy. The preliminary compression allows limiting the number and complexity of the surrogate models, thus leading to a substantial improvement in the efficiency. The feasibility and performance of the proposed approach are investigated by means of two digital link designs with 54 and 115 uncertain parameters, respectively
Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing
We introduce a novel framework of reservoir computing, that is capable of
both connectionist machine intelligence and symbolic computation. Cellular
automaton is used as the reservoir of dynamical systems. Input is randomly
projected onto the initial conditions of automaton cells and nonlinear
computation is performed on the input via application of a rule in the
automaton for a period of time. The evolution of the automaton creates a
space-time volume of the automaton state space, and it is used as the
reservoir. The proposed framework is capable of long short-term memory and it
requires orders of magnitude less computation compared to Echo State Networks.
We prove that cellular automaton reservoir holds a distributed representation
of attribute statistics, which provides a more effective computation than local
representation. It is possible to estimate the kernel for linear cellular
automata via metric learning, that enables a much more efficient distance
computation in support vector machine framework. Also, binary reservoir feature
vectors can be combined using Boolean operations as in hyperdimensional
computing, paving a direct way for concept building and symbolic processing.Comment: Corrected Typos. Responded some comments on section 8. Added appendix
for details. Recurrent architecture emphasize
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