3,029 research outputs found
Linear and Order Statistics Combiners for Pattern Classification
Several researchers have experimentally shown that substantial improvements
can be obtained in difficult pattern recognition problems by combining or
integrating the outputs of multiple classifiers. This chapter provides an
analytical framework to quantify the improvements in classification results due
to combining. The results apply to both linear combiners and order statistics
combiners. We first show that to a first order approximation, the error rate
obtained over and above the Bayes error rate, is directly proportional to the
variance of the actual decision boundaries around the Bayes optimum boundary.
Combining classifiers in output space reduces this variance, and hence reduces
the "added" error. If N unbiased classifiers are combined by simple averaging,
the added error rate can be reduced by a factor of N if the individual errors
in approximating the decision boundaries are uncorrelated. Expressions are then
derived for linear combiners which are biased or correlated, and the effect of
output correlations on ensemble performance is quantified. For order statistics
based non-linear combiners, we derive expressions that indicate how much the
median, the maximum and in general the ith order statistic can improve
classifier performance. The analysis presented here facilitates the
understanding of the relationships among error rates, classifier boundary
distributions, and combining in output space. Experimental results on several
public domain data sets are provided to illustrate the benefits of combining
and to support the analytical results.Comment: 31 page
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
Classifying multispectral data by neural networks
Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data
A sparse multinomial probit model for classification
A recent development in penalized probit modelling using a hierarchical Bayesian approach has led to a sparse binomial (two-class) probit classifier that can be trained via an EM algorithm. A key advantage of the formulation is that no tuning of hyperparameters relating to the penalty is needed thus simplifying the model selection process. The resulting model demonstrates excellent classification performance and a high degree of sparsity when used as a kernel machine. It is, however, restricted to the binary classification problem and can only be used in the multinomial situation via a one-against-all or one-against-many strategy. To overcome this, we apply the idea to the multinomial probit model. This leads to a direct multi-classification approach and is shown to give a sparse solution with accuracy and sparsity comparable with the current state-of-the-art. Comparative numerical benchmark examples are used to demonstrate the method
A Detailed Investigation into Low-Level Feature Detection in Spectrogram Images
Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation—it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com
Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function
This paper presents a number of proofs that
equate the outputs of a Multi-Layer Perceptron
(MLP) classifier and the optimal Bayesian discriminant
function for asymptotically large sets of
statistically independent training samples. Two
broad classes of objective functions are shown to
yield Bayesian discriminant performance. The
first class are “reasonable error measures,” which
achieve Bayesian discriminant performance by
engendering classifier outputs that asymptotically
equate to a posteriori probabilities. This class includes
the mean-squared error (MSE) objective
function as well as a number of information theoretic
objective functions. The second class are
classification figures of merit (CFMmono ), which
yield a qualified approximation to Bayesian discriminant
performance by engendering classifier
outputs that asymptotically identify themaximum
a posteriori probability for a given input. Conditions
and relationships for Bayesian discriminant
functional equivalence are given for both classes
of objective functions. Differences between the
two classes are then discussed very briefly in the
context of how they might affect MLP classifier
generalization, given relatively small training
sets
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