Results 1  10
of
80
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract

Cited by 453 (52 self)
 Add to MetaCart
This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
SBA: a software package for generic sparse bundle adjustment
 ACM Transactions on Mathematical Software
, 2009
"... Foundation for Research and Technologyâ€”Hellas ..."
Hooking Your Solver to AMPL
, 1997
"... This report tells how to make solvers work with AMPL's solve command. It describes an interface library, amplsolver.a, whose source is available from netlib. Examples include programs for listing LPs, automatic conversion to the LP dual (shellscript as solver), solvers for various nonlinear probl ..."
Abstract

Cited by 28 (5 self)
 Add to MetaCart
This report tells how to make solvers work with AMPL's solve command. It describes an interface library, amplsolver.a, whose source is available from netlib. Examples include programs for listing LPs, automatic conversion to the LP dual (shellscript as solver), solvers for various nonlinear problems (with first and sometimes second derivatives computed by automatic differentiation), and getting C or Fortran 77 for nonlinear constraints, objectives and their first derivatives. Drivers for various well known linear, mixedinteger, and nonlinear solvers provide more examples.
MemoryBased Neural Networks For Robot Learning
 Neurocomputing
, 1995
"... This paper explores a memorybased approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their ..."
Abstract

Cited by 27 (8 self)
 Add to MetaCart
This paper explores a memorybased approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memorybased, robot learning, locally weighted regression, nearest neighbor, local models. 1 Introduction An important problem in motor learning is approxim...
The Solution of the Metric STRESS and SSTRESS Problems in Multidimensional Scaling Using Newton's Method
, 1995
"... This paper considers numerical algorithms for finding local minimizers of metric multidimensional scaling problems. Both the STRESS and SSTRESS criteria are considered, and the leading algorithms for each are carefully explicated. A new algorithm, based on Newton's method, is proposed. Translational ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
This paper considers numerical algorithms for finding local minimizers of metric multidimensional scaling problems. Both the STRESS and SSTRESS criteria are considered, and the leading algorithms for each are carefully explicated. A new algorithm, based on Newton's method, is proposed. Translational and rotational indeterminancy is removed by a parametrization that has not previously been used in multidimensional scaling algorithms. In contrast to previous algorithms, a very pleasant feature of the new algorithm is that it can be used with either the STRESS or the SSTRESS criterion. Numerical results are presented. Key words: Metric multidimensional scaling, STRESS criterion, SSTRESS criterion, unconstrained optimization, Newton's method. Department of Computational and Applied Mathematics, Rice University, Houston, TX 772511892. This author was generously supported by a Patricia R. Harris Fellowship. y Department of Computational and Applied Mathematics and Center for Research in...
Derivative Convergence for Iterative Equation Solvers
, 1993
"... this paper, we consider two approaches to computing the desired implicitly defined derivative x ..."
Abstract

Cited by 20 (13 self)
 Add to MetaCart
this paper, we consider two approaches to computing the desired implicitly defined derivative x
A generalized learning paradigm exploiting the structure of feedforward neural networks
 IEEE Trans. Neural Networks
, 1996
"... In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space o ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space of the outputs of the linear blocks (descent in the neuron space), which can be performed using any preferred optimization strategy. In the second step, each linear block is optimized separately by using a Least Squares (LS) criterion. To demonstrate the effectiveness of the new approach, a detailed treatment of a gradient descent in the neuron space is conducted. The main properties of this approach are the higher speed of convergence with respect to methods that employ an ordinary gradient descent in the weight space (Backpropagation, BP), better numerical conditioning and lower computational cost compared to techniques based on the Hessian matrix. The numerical stability is assured by the use of robust LS linear system solvers, operating directly on the input data of each layer. Experimental results obtained in three problems are described, which confirm the effectiveness of the new method.
Implementing Projection Pursuit Learning
, 1996
"... This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a one hidden layer sigmoidal neural network shows why grouping hidden units in a projection pursuit learning network is useful. Learning robot arm inverse dynamics is used as an example problem.
Sizing and least change secant methods
 Department of Combinatorics and Optimization, University of Waterloo
, 1993
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Optimization and Regularization of Nonlinear Least Squares Problems
, 1996
"... An important branch in scientific computing is parameter estimation. Given a mathematical model and observation data, parameters are sought to explain physical properties as well as possible. In order to find these parameters an optimization problem is often formed, frequently a nonlinear least squa ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
An important branch in scientific computing is parameter estimation. Given a mathematical model and observation data, parameters are sought to explain physical properties as well as possible. In order to find these parameters an optimization problem is often formed, frequently a nonlinear least squares problem. This thesis mainly contributes to the development of tools, techniques, and theories for nonlinear least squares problems that lack a welldefined solution. Specifically, the intention is to generalize regularization methods for linear inverse problems to also handle nonlinear inverse problems. The investigation started by considering an exactly rankdeficient problem, i.e., a problem with a dependency among the parameters. It turns out that such a problem can be formulated as a nonlinear minimum norm problem. To solve this optimization problem two regularization methods are proposed: A GaussNewton Tikhonov regularized method and a minimum norm GaussNewton method. It is shown t...