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NIPS
2004
14 years 28 days ago
Incremental Learning for Visual Tracking
Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features to handle appearance variation of...
Jongwoo Lim, David A. Ross, Ruei-Sung Lin, Ming-Hs...
NIPS
2004
14 years 28 days ago
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
Omid Madani, David M. Pennock, Gary William Flake
NIPS
2004
14 years 28 days ago
Planning for Markov Decision Processes with Sparse Stochasticity
Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thr...
NIPS
2004
14 years 28 days ago
Maximum Likelihood Estimation of Intrinsic Dimension
We propose a new method for estimating intrinsic dimension of a dataset derived by applying the principle of maximum likelihood to the distances between close neighbors. We derive...
Elizaveta Levina, Peter J. Bickel
NIPS
2004
14 years 28 days ago
Rate- and Phase-coded Autoassociative Memory
Areas of the brain involved in various forms of memory exhibit patterns of neural activity quite unlike those in canonical computational models. We show how to use well-founded Ba...
Máté Lengyel, Peter Dayan
NIPS
2004
14 years 28 days ago
Joint MRI Bias Removal Using Entropy Minimization Across Images
The correction of bias in magnetic resonance images is an important problem in medical image processing. Most previous approaches have used a maximum likelihood method to increase...
Erik G. Learned-Miller, Parvez Ahammad
NIPS
2004
14 years 28 days ago
Semi-supervised Learning via Gaussian Processes
We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCN...
Neil D. Lawrence, Michael I. Jordan
NIPS
2004
14 years 28 days ago
Beat Tracking the Graphical Model Way
We present a graphical model for beat tracking in recorded music. Using a probabilistic graphical model allows us to incorporate local information and global smoothness constraint...
Dustin Lang, Nando de Freitas
NIPS
2004
14 years 28 days ago
Methods Towards Invasive Human Brain Computer Interfaces
During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paral...
Thomas Navin Lal, Thilo Hinterberger, Guido Widman...
NIPS
2004
14 years 28 days ago
An Application of Boosting to Graph Classification
This paper presents an application of Boosting for classifying labeled graphs, general structures for modeling a number of real-world data, such as chemical compounds, natural lan...
Taku Kudo, Eisaku Maeda, Yuji Matsumoto