1,621 research outputs found
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance
A new computational method for solving fully fuzzy nonlinear matrix equations
Multi formulations and computational methodologies have been suggested to extract solution of fuzzy nonlinear programming problems. However, in some cases the methods which have been utilised in order to find the solution of these problems involve greater complexity. On the basis of the mentioned reason, the current research work is intended towards introduction of a simple method for finding the fuzzy optimal solution related to fuzzy nonlinear issues. The proposed method is validated and is confirmed to be applicable by suggesting some demonstrated examples. The results confirm that the proposed method is so easy to understand and to apply for solving fully fuzzy nonlinear system (FFNS)
Stable Adaptive Control Using New Critic Designs
Classical adaptive control proves total-system stability for control of
linear plants, but only for plants meeting very restrictive assumptions.
Approximate Dynamic Programming (ADP) has the potential, in principle, to
ensure stability without such tight restrictions. It also offers nonlinear and
neural extensions for optimal control, with empirically supported links to what
is seen in the brain. However, the relevant ADP methods in use today -- TD,
HDP, DHP, GDHP -- and the Galerkin-based versions of these all have serious
limitations when used here as parallel distributed real-time learning systems;
either they do not possess quadratic unconditional stability (to be defined) or
they lead to incorrect results in the stochastic case. (ADAC or Q-learning
designs do not help.) After explaining these conclusions, this paper describes
new ADP designs which overcome these limitations. It also addresses the
Generalized Moving Target problem, a common family of static optimization
problems, and describes a way to stabilize large-scale economic equilibrium
models, such as the old long-term energy model of DOE.Comment: Includes general reviews of alternative control technologies and
reinforcement learning. 4 figs, >70p., >200 eqs. Implementation details,
stability analysis. Included in 9/24/98 patent disclosure. pdf version
uploaded 2012, based on direct conversion of the original word/html file,
because of issues of format compatabilit
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Quantum field theory on a growing lattice
We construct the classical and canonically quantized theories of a massless
scalar field on a background lattice in which the number of points--and hence
the number of modes--may grow in time. To obtain a well-defined theory certain
restrictions must be imposed on the lattice. Growth-induced particle creation
is studied in a two-dimensional example. The results suggest that local mode
birth of this sort injects too much energy into the vacuum to be a viable model
of cosmological mode birth.Comment: 28 pages, 2 figures; v.2: added comments on defining energy, and
reference
NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals
This paper develops a new class of nonlinear acceleration algorithms based on
extending conjugate residual-type procedures from linear to nonlinear
equations. The main algorithm has strong similarities with Anderson
acceleration as well as with inexact Newton methods - depending on which
variant is implemented. We prove theoretically and verify experimentally, on a
variety of problems from simulation experiments to deep learning applications,
that our method is a powerful accelerated iterative algorithm.Comment: Under Revie
Electrical impedance imaging in two-phase, gas-liquid flows: 1. Initial investigation
The determination of interfacial area density in two-phase, gas-liquid flows is one of the major elements impeding significant development of predictive tools based on the two-fluid model. Currently, these models require coupling of liquid and vapor at interfaces using constitutive equations which do not exist in any but the most rudimentary form. Work described herein represents the first step towards the development of Electrical Impedance Computed Tomography (EICT) for nonintrusive determination of interfacial structure and evolution in such flows
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