Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
Neural Computation
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Digital Image Warping
Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Estimation of High-Density Regions Using One-Class Neighbor Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Face recognition based on discriminant fractional Fourier feature extraction
Pattern Recognition Letters
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Machine learning approaches to network anomaly detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Efficient instance-based learning on data streams
Intelligent Data Analysis
Locally linear reconstruction for instance-based learning
Pattern Recognition
A virtual metrology system for semiconductor manufacturing
Expert Systems with Applications: An International Journal
Bootstrap based pattern selection for support vector regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Virtual metrology for run-to-run control in semiconductor manufacturing
Expert Systems with Applications: An International Journal
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The ''locally linear reconstruction'' (LLR) provides a principled and k-insensitive way to determine the weights of k-nearest neighbor (k-NN) learning. LLR, however, does not provide a confidence interval for the k neighbors-based reconstruction of a query point, which is required in many real application domains. Moreover, its fixed linear structure makes the local reconstruction model unstable, resulting in performance fluctuation for regressions under different k values. Therefore, we propose a probabilistic local reconstruction (PLR) as an extended version of LLR in the k-NN regression. First, we probabilistically capture the reconstruction uncertainty by incorporating Gaussian regularization prior into the reconstruction model. This prevents over-fitting when there are no informative neighbors in the local reconstruction. We then project data into a higher dimensional feature space to capture the non-linear relationship between neighbors and a query point when a value of k is large. Preliminary experimental results demonstrated that the proposed Bayesian kernel treatment improves accuracy and k-invariance. Moreover, from the experiment on a real virtual metrology data set in the semiconductor manufacturing, it was found that the uncertainty information on the prediction outcomes provided by PLR supports more appropriate decision making.