37 Result(s)
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Article
Odor recognition in robotics applications by discriminative time-series modeling
Odor classification by a robot equipped with an electronic nose (e-nose) is a challenging task for pattern recognition since volatiles have to be classified quickly and reliably even in the case of short measu...
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Chapter and Conference Paper
Local Reject Option for Deterministic Multi-class SVM
Classification with reject option allows classifiers to abstain from the classification of unclear cases. While it has been shown that global reject options are optimal for probabilistic classifiers, local rej...
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Chapter and Conference Paper
Non-negative Kernel Sparse Coding for the Analysis of Motion Data
We are interested in a decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time war** (DTW), w...
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Chapter and Conference Paper
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning
Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple k-nearest neighbor classifier via a convex optimization scheme. However, the ...
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Chapter and Conference Paper
Local Rejection Strategies for Learning Vector Quantization
Classification with rejection is well understood for classifiers which provide explicit class probabilities. The situation is more complicated for popular deterministic classifiers such as learning vector quan...
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Chapter and Conference Paper
Efficient Adaptation of Structure Metrics in Prototype-Based Classification
More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector q...
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Chapter and Conference Paper
Sparse Prototype Representation by Core Sets
Due to the increasing amount of large data sets, efficient learning algorithms are necessary. Also the interpretation of the final model is desirable to draw efficient conclusions from the model results. Proto...
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Chapter and Conference Paper
Secure Semi-supervised Vector Quantization for Dissimilarity Data
The amount and complexity of data increase rapidly, however, due to time and cost constrains, only few of them are fully labeled. In this context non-vectorial relational data given by pairwise (dis-)similarit...
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Chapter and Conference Paper
A Median Variant of Generalized Learning Vector Quantization
We introduce a median variant of the Generalized Learning Vector Quantization (GLVQ) algorithm. Thus, GLVQ can be used for classification problem learning, for which only dissimilarity information between the ...
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Chapter and Conference Paper
Using Nonlinear Dimensionality Reduction to Visualize Classifiers
Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a given finite high-dimensional data set in two dimensions. In this contribution, we address the problem to visualiz...
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Chapter and Conference Paper
Kernel Robust Soft Learning Vector Quantization
Prototype-based classification schemes offer very intuitive and flexible classifiers with the benefit of easy interpretability of the results and scalability of the model complexity. Recent prototype-based mod...
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Chapter and Conference Paper
A Conformal Classifier for Dissimilarity Data
Current classification algorithms focus on vectorial data, given in euclidean or kernel spaces. Many real world data, like biological sequences are not vectorial and often non-euclidean, given by (dis-)similar...
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Chapter and Conference Paper
Patch Processing for Relational Learning Vector Quantization
Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10,9]. An intuitive prototype based classification sche...
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Chapter and Conference Paper
Learning Relevant Time Points for Time-Series Data in the Life Sciences
In the life sciences, short time series with high dimensional entries are becoming more and more popular such as spectrometric data or gene expression profiles taken over time. Data characteristics rule out cl...
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Chapter and Conference Paper
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
For complex data sets, the pairwise similarity or dissimilarity of data often serves as the interface of the application scenario to the machine learning tool. Hence, the final result of training is severely i...
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Chapter and Conference Paper
Relational Extensions of Learning Vector Quantization
Prototype-based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector...
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Chapter and Conference Paper
Accelerating Kernel Neural Gas
Clustering approaches constitute important methods for unsupervised data analysis. Traditionally, many clustering models focus on spherical or ellipsoidal clusters in Euclidean space. Kernel methods extend the...
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Chapter and Conference Paper
A General Framework for Dimensionality Reduction for Large Data Sets
With electronic data increasing dramatically in almost all areas of research, a plethora of new techniques for automatic dimensionality reduction and data visualization has become available in recent years. Th...
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Chapter and Conference Paper
Topographic Map** of Dissimilarity Data
Topographic map** offers a very flexible tool to inspect large quantities of high-dimensional data in an intuitive way. Often, electronic data are inherently non-Euclidean and modern data formats are connect...
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Chapter and Conference Paper
Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis
Matrix neural gas has been proposed as a mathematically well-founded extension of neural gas networks to represent data in terms of prototypes and local principal components in a smooth way. The additional inf...
