Under this unifying formalism a wide range of methods have been developed, dealing with binary and multiclass classification, regression, ranking, clustering and novelty detection to name a few.
Recent developments include statistical tests of dependency and alignments between related domains, such as documents written in different languages. Key to the success of any kernel method is the definition of an appropriate kernel for the data at hand. A well-designed kernel should capture the aspects characterizing similar instances while being computationally efficient. Building on the seminal work by D. Haussler on convolution kernels, a vast literature on kernels for structured data has arisen.
Kernels have been designed for sequences, trees and graphs, as well as arbitrary relational data represented in first or higher order logic. From the representational viewpoint, this allowed to address one of the main limitations of statistical learning approaches, namely the difficulty to deal with complex domain knowledge. Interesting connections between the complementary fields of statistical and symbolic learning have arisen as one of the consequences. Another interesting connection made possible by kernels is between generative and discriminative learning.
Here data are represented with generative models and appropriate kernels are built on top of them to be used in a discriminative setting. Unable to display preview. Download preview PDF. Skip to main content. Advertisement Hide. Kernel Methods for Structured Data. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access.
Conference on Neural Information Processing Systems - Wikipedia
Amari, S. Aronszajn, N. Bakir, G. Berg, C. Borgwardt, K. Boser, B. In: Proc. Collins, M. In: Dietterich, T. Advances in Neural Information Processing Systems Crammer, K. Cristianini, N. Cambridge University Press Google Scholar. De Raedt, L.
Predicting Structured Data
Springer Google Scholar. Durbin, R.
Fletcher, R. In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. From Wikipedia, the free encyclopedia. Dimensionality reduction. Structured prediction. Graphical models Bayes net Conditional random field Hidden Markov. Anomaly detection. Artificial neural networks.
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Related articles. List of datasets for machine-learning research Outline of machine learning. Retrieved NIPS Foundation. Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field.
The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Alexander J.
Structured Prediction Models
This volume presents state-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. This volume presents and analyzes the state-of-the-art in machine learning algorithms and theory in this novel field. Convert currency. Add to Basket. Book Description Condition: New. This is Brand New.