Machine Learning
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Matrix representations, linear transformations, and kernels for disambiguation in natural language
Machine Learning - Tập 74 Số 2 - Trang 133-158 - 2009
In the application of machine learning methods with natural language inputs, the words and their positions in the input text are some of the most important features. In this article, we introduce a framework based on a word-position matrix representation of text, linear feature transformations of the word-position matrices, and kernel functions constructed from the transformations. We consider two......
Learning boolean functions in an infinite attribute space
Machine Learning - Tập 9 - Trang 373-386 - 1992
This paper presents a theoretical model for learning Boolean functions in domains having a large, potentially infinite number of attributes. The model allows an algorithm to employ a rich vocabulary to describe the objects it encounters in the world without necessarily incurring time and space penalties so long as eachindividual object is relatively simple. We show that many of the basic Boolean f......
Inverse reinforcement learning in contextual MDPs
Machine Learning - Tập 110 - Trang 2295-2334 - 2021
We consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the re......
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning - Tập 58 - Trang 5-24 - 2005
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and Super-Parent TAN have demonstrated remarkable error performance. However, both techniques obtain this outcome at a considerable computational cost. We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classi......
Learning sparse gradients for variable selection and dimension reduction
Machine Learning - Tập 87 - Trang 303-355 - 2012
Variable selection and dimension reduction are two commonly adopted approaches for high-dimensional data analysis, but have traditionally been treated separately. Here we propose an integrated approach, called sparse gradient learning (SGL), for variable selection and dimension reduction via learning the gradients of the prediction function directly from samples. By imposing a sparsity constraint ......
Introduction to the special issue on dynamic networks and knowledge discovery
Machine Learning - - 2017
On the Complexity of Function Learning
Machine Learning - Tập 18 - Trang 187-230 - 1995
The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range {0, 1}. Much less is known about the theory of learning functions with a larger range such as
$$\mathbb{N}$$
or
......
A process for predicting manhole events in Manhattan
Machine Learning - Tập 80 - Trang 1-31 - 2010
We present a knowledge discovery and data mining process developed as part of the Columbia/Con Edison project on manhole event prediction. This process can assist with real-world prioritization problems that involve raw data in the form of noisy documents requiring significant amounts of pre-processing. The documents are linked to a set of instances to be ranked according to prediction criteria. I......
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