8,255 research outputs found
Mixtures of Spatial Spline Regressions
We present an extension of the functional data analysis framework for
univariate functions to the analysis of surfaces: functions of two variables.
The spatial spline regression (SSR) approach developed can be used to model
surfaces that are sampled over a rectangular domain. Furthermore, combining SSR
with linear mixed effects models (LMM) allows for the analysis of populations
of surfaces, and combining the joint SSR-LMM method with finite mixture models
allows for the analysis of populations of surfaces with sub-family structures.
Through the mixtures of spatial splines regressions (MSSR) approach developed,
we present methodologies for clustering surfaces into sub-families, and for
performing surface-based discriminant analysis. The effectiveness of our
methodologies, as well as the modeling capabilities of the SSR model are
assessed through an application to handwritten character recognition
A generic optimising feature extraction method using multiobjective genetic programming
In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved
Adaptive detection of volunteer potato plants in sugar beet fields
Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advanc
Fast DD-classification of functional data
A fast nonparametric procedure for classifying functional data is introduced.
It consists of a two-step transformation of the original data plus a classifier
operating on a low-dimensional hypercube. The functional data are first mapped
into a finite-dimensional location-slope space and then transformed by a
multivariate depth function into the -plot, which is a subset of the unit
hypercube. This transformation yields a new notion of depth for functional
data. Three alternative depth functions are employed for this, as well as two
rules for the final classification on . The resulting classifier has
to be cross-validated over a small range of parameters only, which is
restricted by a Vapnik-Cervonenkis bound. The entire methodology does not
involve smoothing techniques, is completely nonparametric and allows to achieve
Bayes optimality under standard distributional settings. It is robust,
efficiently computable, and has been implemented in an R environment.
Applicability of the new approach is demonstrated by simulations as well as a
benchmark study
Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
We propose a high dimensional classification method that involves
nonparametric feature augmentation. Knowing that marginal density ratios are
the most powerful univariate classifiers, we use the ratio estimates to
transform the original feature measurements. Subsequently, penalized logistic
regression is invoked, taking as input the newly transformed or augmented
features. This procedure trains models equipped with local complexity and
global simplicity, thereby avoiding the curse of dimensionality while creating
a flexible nonlinear decision boundary. The resulting method is called Feature
Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by
generalizing the Naive Bayes model, writing the log ratio of joint densities as
a linear combination of those of marginal densities. It is related to
generalized additive models, but has better interpretability and computability.
Risk bounds are developed for FANS. In numerical analysis, FANS is compared
with competing methods, so as to provide a guideline on its best application
domain. Real data analysis demonstrates that FANS performs very competitively
on benchmark email spam and gene expression data sets. Moreover, FANS is
implemented by an extremely fast algorithm through parallel computing.Comment: 30 pages, 2 figure
Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project
The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth
- …