821 resultados para Learning methods
Resumo:
Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
Resumo:
Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.
Resumo:
In the fierce competition of today‟s business world an organization‟s capacity to learn maybe its only competitive advantage. This research aims at increasing the understanding on how organizational learning from the customer happens in technology companies. In doing so it provides a synthesized definition of organizational learning and investigates processes of organizational learning within technology companies. A qualitative research method and in-depth interviews with different sized high technology companies, as applied here, enables in-depth study of the learning processes. Research contributes to the understanding of what type of knowledge firms acquire, how new knowledge is transferred and used in a learning firm‟s routines and processes. Research findings show that SMEs and large size companies also, depending on their position in the software value chain, consider different knowledge types as most important and that they use different learning methods to acquire knowledge from their customers.
Resumo:
The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.
Resumo:
A significant number of adults in adult literacy programs in Ontario have specific learning difficulties. This study sought to examine the holistic factors that contributed to these learners achieving their goals. Through a case study design, the data revealed that a combination of specific learning methods and strategies, along with particular characteristics of the instructor, participant, and class, and the evidence of self-transformation all seemed to contribute to the participant's success in the program. Instructor-directed teaching and cooperative learning were the main learning methods used in the class. General learning strategies employed were the use of core curriculum and authentic documents, and using phonics, repetition, assistive resources, and using activities that appealed to various learning styles. The instructor had a history of both professional development in the area of learning disabilities as well as experience working with learners who had specific learning difficulties. There also seemed to be a goodness of fit between the participant and the instructor. Several characteristics of the participant seemed to aid in his success: his positive self-esteem, self-advocacy skills, self-determination, self-awareness, and the fact that he enjoyed learning. The size (3-5 people) and type of class (small group) also seemed to have an impact. Finally, evidence that the participant went through a self-transformation seemed to contribute to a positive learner identity. These results have implications for practice, theory, and further research in adult education.
Resumo:
The Welsh private and third sectors are heavily dependent on SMEs. Consequently the performance of SMEs is critical to the performance of the Welsh economy. Substantial public funds, particularly from European Structural Funds, have been allocated to support these since 2000. The majority of programmes thus funded have been led from within the Welsh Government. This paper reports interim evaluation findings from one intervention led by two Welsh higher education institutions (HEIs), namely the LEAD Wales programme. The programme is an extended intervention to support the leadership skills of owner-managers and incorporates a range of learning methods, including formal masterclasses, but emphasizes situated and experiential learning through action learning, coaching and peer-to-peer exchange exercises. The programme’s impact is assessed on the experiences of 325 participants, of whom 217 have completed the programme. The paper concludes that situated learning methods, through which participants are able to draw from shared history and experience over an extended period are critical to programme success. By contrast, short-term thematic teaching, based around more formal, hierarchical learning is less likely to yield significant and sustainable economic benefits. The implications of this for business support in Wales are discussed.
Resumo:
With the rapid advancement of the webtechnology, more and more educationalresources, including software applications forteaching/learning methods, are available acrossthe web, which enables learners to access thelearning materials and use various ways oflearning at any time and any place. Moreover,various web-based teaching/learning approacheshave been developed during the last decade toenhance the capability of both educators andlearners. Particularly, researchers from bothcomputer science and education are workingtogether, collaboratively focusing ondevelopment of pedagogically enablingtechnologies which are believed to improve theinfrastructure of education systems andprocesses, including curriculum developmentmodels, teaching/learning methods, managementof educational resources, systematic organizationof communication and dissemination ofknowledge and skills required by and adapted tousers. Despite of its fast development, however,there are still great gaps between learningintentions, organization of supporting resources,management of educational structures,knowledge points to be learned and interknowledgepoint relationships such as prerequisites,assessment of learning outcomes, andtechnical and pedagogic approaches. Moreconcretely, the issues have been widelyaddressed in literature include a) availability andusefulness of resources, b) smooth integration ofvarious resources and their presentation, c)learners’ requirements and supposed learningoutcomes, d) automation of learning process interms of its schedule and interaction, and e)customization of the resources and agilemanagement of the learning services for deliveryas well as necessary human interferences.Considering these problems and bearing in mindthe advanced web technology of which weshould make full use, in this report we willaddress the following two aspects of systematicarchitecture of learning/teaching systems: 1)learning objects – a semantic description andorganization of learning resources using the webservice models and methods, and 2) learningservices discovery and learning goals match foreducational coordination and learning serviceplanning.
Resumo:
This thesis focuses on the adaptation of formal education to people’s technology- use patterns, theirtechnology-in-practice, where the ubiquitous use of mobile technologies is central. The research question is: How can language learning practices occuring in informal learning environments be effectively integrated with formal education through the use of mobile technology? The study investigates the technical, pedagogical, social and cultural challenges involved in a design science approach. The thesis consists of four studies. The first study systematises MALL (mobile-assisted language learning) research. The second investigates Swedish and Chinese students’ attitudes towards the use of mobile technology in education. The third examines students’ use of technology in an online language course, with a specific focus on their learning practices in informal learning contexts and their understanding of how this use guides their learning. Based on the findings, a specifically designed MALL application was built and used in two courses. Study four analyses the app use in terms of students’ perceived level of self-regulation and structuration. The studies show that technology itself plays a very important role in reshaping peoples’ attitudes and that new learning methods are coconstructed in a sociotechnical system. Technology’s influence on student practices is equally strong across borders. Students’ established technologies-in-practice guide the ways they approach learning. Hence, designing effective online distance education involves three interrelated elements: technology, information, and social arrangements. This thesis contributes to mobile learning research by offering empirically and theoretically grounded insights that shift the focus from technology design to design of information systems.
Resumo:
It is now-a-days more and more common in the academic world to use new forms of “learning-tools”. One of those is the “reflection protocol”, which usually consist of a few pages of freely written text, related to something the students have read. There seems to be a lot of different opinions about the value to use this method. Some teachers and students are enthusiastic and others are rather critical. To write a “reflection protocol” is not in the first place to do a summery, a review, not even to analyze a text. Instead it is about to write down thoughts and questions that comes up as a result of the reading. It is also about doing associations, reflections and to interpret a text and relate this to a theme of some kind. The purpose to use “reflection protocols” is, as we see it, mainly for the student to practice independent thinking from a scientific point of view, but it also gives a possibility to a better understanding of another person’s thinking. This seems to open up for a fruitful dialogue and a way to learn. We will in this paper discuss if that could be the case.
Resumo:
On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.
Resumo:
Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.
Resumo:
The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.
Resumo:
Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.
Resumo:
Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.