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We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional(More)
This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied to exponential sized representations of parse trees, such as the “all subtrees” (DOP) representation described by (Bod 1998), or a representation tracking all sub-fragments of a tagged(More)
MOTIVATION DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists(More)
In this paper we examine ensemble methods for regression that leverage or “boost” base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its strong theoretical guarantees. We present several(More)
In this paper we examine master regression algorithms that leverage base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its good theoretical bounds. We present three gradient descent(More)
Œe success of deep learning depends on €nding an architecture to €t the task. As deep learning has scaled up to more challenging tasks, the architectures have become dicult to design by hand. Œis paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to(More)
In the case of different diseases classification is an important aspect so that one can find the infected set efficiently. In this paper three different dataset named Leukemia, Lung Cancer and Prostate from the UCI machine learning repository are considered and apply efficient association based ant colony optimization for improving the classification(More)
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