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Support vector learning for ordinal regression
We investigate the problem of predicting variables of ordinal scale. Expand
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Soft Learning Vector Quantization
We derive two variants of LVQ using a gaussian mixture ansatz and derive a learning rule using gradient descent. Expand
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New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks
Exact geometrical reconstructions of neuronal architecture are indispensable for the investigation of neuronal function. Neuronal shape is important for the wiring of networks, and dendriticExpand
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Invariant computations in local cortical networks with balanced excitation and inhibition
Cortical computations critically involve local neuronal circuits. The computations are often invariant across a cortical area yet are carried out by networks that can vary widely within an areaExpand
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Classification on Pairwise Proximity Data
We investigate the problem of learning a classification task on data represented in terms of their pairwise proximities. Expand
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Support Vector Machines for Dyadic Data
We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. Expand
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A new summarization method for affymetrix probe level data
We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. Expand
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Models of Orientation and Ocular Dominance Columns in the Visual Cortex: A Critical Comparison
We introduce several new measures for comparing experimental and model map data that reveal important differences between models that can help to improve current models. Expand
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Self-organizing maps: Generalizations and new optimization techniques
We offer three algorithms for the generation of topographic mappings to the practitioner of unsupervised data analysis based on the minimization of a cost function which is performed using an EM algorithm and deterministic annealing. Expand
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Quadratic optimization for simultaneous matrix diagonalization
In this paper, we present a new algorithm called QDIAG that splits the overall optimization problem into a sequence of simpler second order subproblems and show that the algorithm converges fast and reliably. Expand
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