Learning multiple views with orthogonal denoising autoencoders

Multi-view learning techniques are necessary when data is described by multiple distinct feature sets because single-view learning algorithms tend to overt on these high-dimensional data. Prior successful approaches followed either consensus or complementary principles. Recent work has focused on learning both the shared and private latent spaces of views in order to take advantage of both principles. However, these methods can not ensure that the latent spaces are strictly independent through encouraging the orthogonality in their objective functions. Also little work has explored representation learning techniques for multiview learning. In this paper, we use the denoising autoencoder to learn shared and private latent spaces, with orthogonal constraints | disconnecting every private latent space from the remaining views. Instead of computationally expensive optimization, we adapt the backpropagation algorithm to train our model

Exchange rate modelling for E-negotiators using text mining techniques

The Curious Negotiator project aims at the automation (to the extent possible) of the delivery and use of information by negotiation agents in electronic market environment. This chapter presents a framework for using text mining agents to provide processed online information to negotiation agents. It includes a news extraction algorithm, a quantitative process model based on the extracted news information, which is exemplified by an exchange rate prediction model, and a communication protocol between data mining agents and negotiation agents. This information is critical for the negotiation agents to form their negotiation strategies. © Springer-Verlag Berlin Heidelberg 2007

Information Extraction in Structured Documents using Tree Automata Induction

Information extraction (IE) addresses the problem of extracting speci c information from a collection of documents. Much of the previous work for IE from structured documents formatted in HTML or XML uses techniques for IE from strings, such as grammar and automata induction. However, HTML and XML documents have a tree structure

Learn-and-Optimize: a Parameter Tuning Framework for Evolutionary AI Planning

Abstract. Learn-and-Optimize (LaO) is a generic surrogate based method for parameter tuning combining learning and optimization. In this paper LaO is used to tune Divide-and-Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate this relation to unknown instances in the same domain. Moreover, the learned knowledge is used as a surrogate-model to accelerate the search for the optimal parameters. The proposed implementation of LaO uses an Artificial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimization. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited amount of meaningful features that are available to describe the instances. However, the learned model reaches almost the same performance on the test instances, which means that it is capable of generalization.