Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
暂无分享,去创建一个
[1] B. S. Manjunath,et al. Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[2] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[3] M. Gerstein,et al. A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome. , 2000, Journal of molecular biology.
[4] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[5] I. Daubechies. Orthonormal bases of compactly supported wavelets , 1988 .
[6] K. Chou,et al. Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location* , 2002, The Journal of Biological Chemistry.
[7] Gavin MacBeath,et al. Protein microarrays and proteomics , 2002, Nature Genetics.
[8] Peer Bork,et al. Predicting protein cellular localization using a domain projection method. , 2002, Genome research.
[9] Robert F. Murphy,et al. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..
[10] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[11] Giorgio Valentini,et al. Ensembles of Learning Machines , 2002, WIRN.
[12] Robert P. W. Duin,et al. Is independence good for combining classifiers? , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.
[13] B. S. Manjunath,et al. A comparison of wavelet transform features for texture image annotation , 1995, Proceedings., International Conference on Image Processing.
[14] K. Nakai. Protein sorting signals and prediction of subcellular localization. , 2000, Advances in protein chemistry.
[15] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[16] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[17] Zhirong Sun,et al. Support vector machine approach for protein subcellular localization prediction , 2001, Bioinform..
[18] Kai Huang,et al. Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images , 2003, SPIE BiOS.
[19] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.
[20] Samy Bengio,et al. Torch: a modular machine learning software library , 2002 .
[21] Ulrich H.-G. Kreßel,et al. Pairwise classification and support vector machines , 1999 .
[23] Robert F. Murphy,et al. Location proteomics: building subcellular location trees from high-resolution 3D fluorescence microscope images of randomly tagged proteins , 2003, SPIE BiOS.
[24] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[25] M V Boland,et al. Toward objective selection of representative microscope images. , 1999, Biophysical journal.
[26] A. Danckaert,et al. Automated Recognition of Intracellular Organelles in Confocal Microscope Images , 2002, Traffic.
[27] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[28] David D. Denison,et al. Nonlinear estimation and classification , 2003 .
[29] Anthony Ralston,et al. Statistical Methods for Digital Computers. , 1980 .
[30] John G. Daugman,et al. Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..
[31] Josef Kittler,et al. Fusion of multiple experts in multimodal biometric personal identity verification systems , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.
[32] Martin Norin,et al. Structural proteomics: developments in structure-to-function predictions. , 2002, Trends in biotechnology.
[33] Steven R. Waterhouse,et al. Classification and Regression using Mixtures of Experts , 1997 .
[34] Robert F. Murphy,et al. Automated determination of protein subcellular locations from 3D fluorescence microscope images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.
[35] Robert F. Murphy,et al. Towards a Systematics for Protein Subcellular Location: Quantitative Description of Protein Localization Patterns and Automated Analysis of Fluorescence Microscope Images , 2000, ISMB.
[36] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[37] S. Brunak,et al. SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites , 1997 .
[38] Robert F. Murphy,et al. Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images , 2003, J. VLSI Signal Process..
[39] John C Reed,et al. Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools , 2002, Journal of cellular biochemistry. Supplement.
[40] Maria Marinaro,et al. Neural Nets WIRN Vietri-01 , 2002, Perspectives in Neural Computing.