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Handwritten digit recognition by combined classifiers

M. Breukelen, Robert P. W. Duin, David M. J. Tax, J. E. den Hartog (1998)

Kybernetika

Classifiers can be combined to reduce classification errors. We did experiments on a data set consisting of different sets of features of handwritten digits. Different types of classifiers were trained on these feature sets. The performances of these classifiers and combination rules were tested. The best results were acquired with the mean, median and product combination rules. The product was best for combining linear classifiers, the median for k -NN classifiers. Training a classifier on all features...

How the initialization affects the stability of the қ-means algorithm

Sébastien Bubeck, Marina Meilă, Ulrike von Luxburg (2012)

ESAIM: Probability and Statistics

We investigate the role of the initialization for the stability of the қ-means clustering algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known as Lloyd algorithm). In particular we leverage on the property that this algorithm can get stuck in local optima of the қ-means objective function. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations lead to the same local optimum, and when they...

How the initialization affects the stability of the қ-means algorithm∗

Sébastien Bubeck, Marina Meilă, Ulrike von Luxburg (2012)

ESAIM: Probability and Statistics

We investigate the role of the initialization for the stability of the қ-means clustering algorithm. As opposed to other papers, we consider the actual қ-means algorithm (also known as Lloyd algorithm). In particular we leverage on the property that this algorithm can get stuck in local optima of the қ-means objective function. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations...

How the result of graph clustering methods depends on the construction of the graph

Markus Maier, Ulrike von Luxburg, Matthias Hein (2013)

ESAIM: Probability and Statistics

We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph clustering algorithm to find a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) influences the outcome of the final clustering result. To this end we study the convergence of cluster quality measures...

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