Learning Methods

This paper describes a technique for both constituent and dependency parsing. Parsing proceeds by adding functional labels to the output of a constituent parser trained on the French Treebank in order to further extract typed dependencies. On the one hand we specify on formal and linguistic grounds the nature of the dependencies to output as well as the conversion algorithm from the French Treebankto this dependency representation. On the other hand, we describe a class of algorithms that allows to perform the automatic labeling of the functions from the output of a constituent based parser. We specificall y focus on discriminative learning methods for functional labelling. Mots-clés : Analyseur syntaxique statistique, analyse en constituant s/dépendances, éti- quetage en fonctions grammaticales.

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This paper describes a technique for both constituent and dependency parsing. Parsing proceeds by adding functional labels to the output of a constituent parser trained on the French Treebank in order to further extract typed dependencies. On the one hand we specify on formal and linguistic grounds the nature of the dependencies to output as well as the conversion algorithm from the French Treebankto this dependency representation. On the other hand, we describe a class of algorithms that allows to perform the automatic labeling of the functions from the output of a constituent based parser. We specificall y focus on discriminative learning methods for functional labelling. Mots-clés : Analyseur syntaxique statistique, analyse en constituant s/dépendances, éti- quetage en fonctions grammaticales.

This paper proposes a framework for the training of learning systems for regression when (i) the number of examples is small and contains interdependencies, and (ii) each sample consists of large quantities of discrete data that are functional in nature. The objective is to achieve robust yet nonlinear relations between inputs and outputs. In this study, laser scans of the

Sunday Olusanya Olatunji Intelligent Software Engineering Laboratory , Faculty of Computer Science & Information Systems, University of Technology Malaysia, 81310 UTM Skudai, Johor, Malaysia (oluolatunji.aadalllrmglllail.colll) ... Dr. Ali Selarnat Intelligent Software ...

There is a growing interest in the use of simulations and games in Dutch higher education. This development is based on the perception that students belong to the ‘gamer generation’ or ‘net generation’: a generation that has grown up with computer games and other technology affecting their preferred learning styles, social interaction patterns and technology use generally. It is often argued that in education this generation prefers active, collaborative and technology-rich learning, i.e. learning methods that involve extensive computer use and collaboration among students. Gaming is then proposed as a new teaching method which addresses these requirements. This article presents the results of a survey which studied whether this discourse is also applicable to higher education students from the Netherlands and whether games, considered as active, collaborative and technology-rich learning experiences, are of greater importance in the formal education of today’s students. Of 1432 respondents from eight Dutch institutes of higher education surveyed between 2005 and 2009, about 25% fit our criteria of being a clear representative of the net generation. Furthermore, our analysis shows that there is little difference, and no statistically significant difference, in active, collaborative and technology-rich learning preferences between the representatives and non-representatives of the net generation. Furthermore, no large or statistically significant differences were found between representatives and non-representatives of the net generation with respect to the value they accorded to gaming in education. Overall our dataset did not fit the expectations raised by the net generation theory, with the percentage of students who fit the criteria being much lower than expected. However, regardless of whether they represented the net generation or not, in general our respondents preferred collaborative and technology-rich learning and deemed games a valuable teaching method.

4th International Conference on Machine Learning & Applications (CMLA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

Learning a large number of simple local concepts is both faster and easier than learning a single global concept. Inspired by this principle of divide and conquer, a number of modular learning approaches have been proposed by the computational intelligence community. In modular learning, the classification/regression/clustering problem is first decomposed into a number of simpler subproblems, a module is learned for each of these subproblems, and finally their results are integrated by a suitable combining method. Mixtures of experts and clustering are two of the techniques that are describable in this paradigm. In this paper we present a broad framework for Generalized Associative Modular Learning Systems (GAMLS). Modularity is introduced through soft association of each training pattern with every module. The coupled problems of learning the module parameters and learning associations are solved iteratively using deterministic annealing. Starting at a high temperature with only one module, GAMLS framework automatically evolves the required number of modules through a systematic growing and pruning technique. Each phase begins by splitting every module in the previous phase into two, updating these new modules and then pruning and merging any redundant modules. A phase transition is induced by temperature decay. A number of existing modular learning problems, both unsupervised (clustering, mixture model density, mixture of principal components) and supervised (mixture of experts, radial basis function networks), can be effectively tackled in GAMLS. Case studies for clustering and regression using mixture of experts are provided for a number of datasets showing the efficacy of the GAMLS framework in evolving the right number of modules, inducing interpretable localizations among modules and robustness of the solution obtained. More importantly, this framework provides a unifying view for understanding and characterizing modular learning methods.

The main goal of this study is to indicate the strategies' types that are employed by Iraqi EFL students to learn Vocabulary. To achieve this aim, the author wants to show which methods that are employed most repeatedly by Iraqi EFL students at University of Technology? Tests of vocabulary(pre/post-test) were adopted in this research to get the aim of the experiment. The author used tests to indicate the influence of meta-cognitive methods on the process of learning English vocabulary. Tests of this study relied on Nation's multiple-choice test of vocabulary knowledge (1990). The researcher asked thirty Iraqi EFL students from University of Technology to be as participants in the current study. Fifteen was the number of male and the same number was for female students. The results indicated that determination strategies were most frequently employed than other types among Iraqi EFL students. On the other hand, using metacognitive strategies were the least frequently among the students.

This paper explains why learning to solve constraint problems is so difficult, and describes a set of methods that has been effective on a broad variety of problem classes. The primary focus is on learning an effective search algorithm as a weighted subset of ordering heuristics. Experiments show the impact of several novel techniques on a variety of problems. When a planning problem is cast as a constraint satisfaction problem (CSP), it can use the representational expressiveness and inference power inherent in constraint programming (Nareyek et al. 2005). If such an encoding lacks the necessary planning knowledge, a program might learn effective solution methods. Our thesis is that it is possible to learn to solve constraint satisfaction problems from experience. In this scenario, a program is given a set of CSPs and a set of search heuristics. It is then expected to learn an effective search algorithm, represented as a weighted combination of some subset of those heuristics. From...