Applicability and interpretability of Ward's hierarchical agglomerative clustering with or without contiguity constraints
Hierarchical agglomerative clustering (HAC) with Ward's linkage has been widely used
since its introduction by Ward (Journal of the American Statistical Association, 58 (301), 236 …
since its introduction by Ward (Journal of the American Statistical Association, 58 (301), 236 …
Model-based methods for continuous and discrete global optimization
The use of surrogate models is a standard method for dealing with complex real-world
optimization problems. The first surrogate models were applied to continuous optimization …
optimization problems. The first surrogate models were applied to continuous optimization …
Online estimation of similarity matrices with incomplete data
The similarity matrix measures pairwise similarities between a set of data points and is an
essential concept in data processing, routinely used in practical applications. Obtaining a …
essential concept in data processing, routinely used in practical applications. Obtaining a …
[HTML][HTML] Polynomial kernel learning for interpolation kernel machines with application to graph classification
Since all training data is interpolated, interpolating classifiers have zero training error.
However, recent work provides compelling reasons to investigate these classifiers, including …
However, recent work provides compelling reasons to investigate these classifiers, including …
[HTML][HTML] Indefinite kernels in least squares support vector machines and principal component analysis
Because of several successful applications, indefinite kernels have attracted many research
interests in recent years. This paper addresses indefinite learning in the framework of least …
interests in recent years. This paper addresses indefinite learning in the framework of least …
Alignment-free sequence comparison: A systematic survey from a machine learning perspective
KS Bohnsack, M Kaden, J Abel… - IEEE/ACM Transactions …, 2022 - ieeexplore.ieee.org
The encounter of large amounts of biological sequence data generated during the last
decades and the algorithmic and hardware improvements have offered the possibility to …
decades and the algorithmic and hardware improvements have offered the possibility to …
Metric nearness made practical
Given a square matrix with noisy dissimilarity measures between pairs of data samples, the
metric nearness model computes the best approximation of the matrix from a set of valid …
metric nearness model computes the best approximation of the matrix from a set of valid …
Metric and non-metric proximity transformations at linear costs
A Gisbrecht, FM Schleif - Neurocomputing, 2015 - Elsevier
Abstract Domain specific (dis-) similarity or proximity measures used eg in alignment
algorithms of sequence data are popular to analyze complicated data objects and to cover …
algorithms of sequence data are popular to analyze complicated data objects and to cover …
Fast deterministic approximation of symmetric indefinite kernel matrices with high dimensional datasets
Kernel methods are used frequently in various applications of machine learning. For large-
scale high dimensional applications, the success of kernel methods hinges on the ability to …
scale high dimensional applications, the success of kernel methods hinges on the ability to …
Learning in reproducing kernel Kreın spaces
We formulate a novel regularized risk minimization problem for learning in reproducing
kernel Kre {ı̆} n spaces and show that the strong representer theorem applies to it. As a …
kernel Kre {ı̆} n spaces and show that the strong representer theorem applies to it. As a …