nips nips2009 nips2009-260 nips2009-260-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell
Abstract: We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words. 1
Bart, E., & Ullman, S. (2005). Cross-generalization: learning novel classes from a single example by feature replacement. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 1, 672–679 vol. 1. Ciaccia, P., & Patella, M. (2000). PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces. Data Engineering, International Conference on, 244. Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research. Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009). Describing objects by their attributes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning. Springer. Kay, K. N., Naselaris, T., Prenger, R. J., & Gallant, J. L. (2008). Identifying natural images from human brain activity. Nature, 452, 352–355. Lampert, C. H., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Larochelle, H., Erhan, D., & Bengio, Y. (2008). Zero-data learning of new tasks. AAAI Conference on Artificial Intelligence. Mitchell, T., et al. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 1191–1195. Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill. Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., & Newman, S. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57, 145–175. Plaut, D. C. (2002). Graded modality-specific specialization in semantics: A computational account of optic aphasia. Cognitive Neuropsychology, 19, 603–639. Snodgrass, J., & Vanderwart, M. (1980). A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 174–215. Torralba, A., & Murphy, K. P. (2007). Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell., 29, 854–869. van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579–2605. Waibel, A. (1989). Modular construction of time-delay neural networks for speech recognition. Neural Computation, 1, 39–46. Wilson, M. (1988). The MRC psycholinguistic database: Machine readable dictionary, version 2. Behavioral Research Methods, 6–11. 9