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» Forecasting high-dimensional data
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ICANN
1997
Springer
15 years 1 months ago
Topology Representing Networks for Intrinsic Dimensionality Estimation
Abstract. In this paper we compare two methods for intrinsic dimensionality (ID) estimation based on optimally topology preserving maps (OTPMs). The rst one is a direct approach, w...
Jörg Bruske, Gerald Sommer
84
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AAAI
2007
15 years 1 days ago
A Randomized String Kernel and Its Application to RNA Interference
String kernels directly model sequence similarities without the necessity of extracting numerical features in a vector space. Since they better capture complex traits in the seque...
Shibin Qiu, Terran Lane, Ljubomir J. Buturovic
AAAI
2010
14 years 11 months ago
Conformal Mapping by Computationally Efficient Methods
Dimensionality reduction is the process by which a set of data points in a higher dimensional space are mapped to a lower dimension while maintaining certain properties of these p...
Stefan Pintilie, Ali Ghodsi
NIPS
2008
14 years 11 months ago
The Recurrent Temporal Restricted Boltzmann Machine
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e., generate nice-looking samples of) several very hi...
Ilya Sutskever, Geoffrey E. Hinton, Graham W. Tayl...
65
Voted
NIPS
2007
14 years 11 months ago
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer f...
Matthias Bethge, Philipp Berens