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CONTENTS 7 Acknowledgments Many people gave substantial suggestions to improve the contents of this book. These are, in alphabetic order, Introduction During the past few years, several of us have been asked many times about references on finite tree automata. On one hand, this is the witness of the liveness of this field. On the other hand, it was(More)
HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt età la diffusion(More)
In the general framework of semi-supervised learning from labeled and unlabeled data, we consider the specific problem of learning from a pool of positive data, without any negative data but with the help of unlabeled data. We study a naive Bayes algorithm PNB from positive and unlabeled examples. Then, we consider the case where the number of positive(More)
We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction. We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (Nstts), a particular class of tree au-tomata that we introduce. We prove that(More)
In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the(More)
We present an original approach to the automatic induction of wrappers for sources of the hidden Web that does not need any human supervision. Our approach only needs domain knowledge expressed as a set of concept names and concept instances. There are two parts in extracting valuable data from hidden-Web sources: understanding the structure of a given HTML(More)
We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultaneously , by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost environment with Multi-Task Decision Trees as weak classifiers. We first adapt the well known(More)
Tree automata based algorithms are essential in many fields in computer science such as verification, specification, program analysis. They become also essential for databases with the development of standards such as XML. In this paper, we define new classes of non de-terministic tree automata, namely residual finite tree automata (RFTA). In the bottom-up(More)
W e present a decision procedure, based on tree automata techniques, for satisfiability of systems of set constraints including negated subset relationships. This result extends all previous works on set constraints solving (Heintze and Jaffar [HJ$Oa] ; Aiken and Wimmers [AW$2] ; Gilleron, Tison and Tom-masi [GTT93a] ; Bachmair, Ganzinger and Wald-mann [BG(More)