A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Desarrollo de un sistema de e-Learning para Nintendo DS

El proyecto trata de crear un software que dinámicamente nos proporcione exámenes o pruebas dependiendo de nuestro nivel de conocimientos actual. Estos exámenes se cargarán a través de un fichero XML configurable, lo que nos permitirá poner a prueba nuestros conocimientos en el tema que deseemos. El software se desarrollará en Nintendo DS, para aprovechar las prestaciones que nos ofrece de serie: doble pantalla, pantalla táctil, portabilidad.

Interoperability issues between learning object repositories and metadata harvesters

In this paper we describe an open learning object repository on Statistics based on DSpace which contains true learning objects, that is, exercises, equations, data sets, etc. This repository is part of a large project intended to promote the use of learning object repositories as part of the learning process in virtual learning environments. This involves the creation of a new user interface that provides users with additional services such as resource rating, commenting and so. Both aspects make traditional metadata schemes such as Dublin Core to be inadequate, as there are resources with no title or author, for instance, as those fields are not used by learners to browse and search for learning resources in the repository. Therefore, exporting OAI-PMH compliant records using OAI-DC is not possible, thus limiting the visibility of the learning objects in the repository outside the institution. We propose an architecture based on ontologies and the use of extended metadata records for both storing and refactoring such descriptions.

The Role of model uncertainty and learning in the U.S. postwar policy response to oil prices

This paper studies optimal monetary policy in a framework that explicitly accounts for policymakers' uncertainty about the channels of transmission of oil prices into the economy. More specfically, I examine the robust response to the real price of oil that US monetary authorities would have been recommended to implement in the period 1970 2009; had they used the approach proposed by Cogley and Sargent (2005b) to incorporate model uncertainty and learning into policy decisions. In this context, I investigate the extent to which regulator' changing beliefs over different models of the economy play a role in the policy selection process. The main conclusion of this work is that, in the specific environment under analysis, one of the underlying models dominates the optimal interest rate response to oil prices. This result persists even when alternative assumptions on the model's priors change the pattern of the relative posterior probabilities, and can thus be attributed to the presence of model uncertainty itself.

Análisis de una iniciativa estudiantil de investigación surgida en Criptogamia, una asignatura optativa de segundo ciclo de Biología = Analysis of a students’ research initiative originating in Cryptogamy, an elective course in the last two years of the Biology degree

Presentamos una experiencia exitosa de aprendizaje que partió de Criptogamia (asignatura optativa de segundo ciclo de Biología), que dio lugar a un proyecto de investigación gestionado por los propios alumnos. La iniciativa se consolidó estableciendo una Asociación de Estudiantes centrada en investigación y divulgación. En poco tiempo, los participantes han presentado comunicaciones científicas, y organizado actividades dirigidas a diversos públicos, dentro y fuera de la comunidad universitaria. Actualmente se plantea una colaboración multidisciplinar con otros organismos de investigación y la extensión de su ámbito de estudio. Abordamos su incidencia en el aprendizaje en varios aspectos: científico (técnicas específicas, rigor, búsqueda de información e interpretación de resultados), comunicativo (estructuración y presentación de la información obtenida, para diversos públicos), y organizativo, incluyendo el trabajo en equipo. Aunque de carácter espontáneo, esta experiencia muestra rasgos evaluables en cuanto a sus posibilidades para otras asignaturas. Analizamos las características y planteamiento de esta optativa, el perfil de sus alumnos, y el contexto universitario que la acoge. Detectamos como factores principales los aspectos participativos de la asignatura, la cohesión del grupo, el carácter voluntario de la implicación, los beneficios percibidos por los estudiantes, y la disponibilidad de recursos humanos (supervisión) y materiales (equipamiento y subvenciones).

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Cooperative learning: learning to cooperate, cooperating to learn

This research project is an attempt to give arguments in favour of using cooperative learning activities in FL classrooms as an effective approach to learning. The arguments offered are presented from two different perspectives: the first one is based on the empirical study of three students working together to achieve a common goal. The second one is a compilation of the trainee teacher's experiences during her practicum periods in a high school regarding group work. This part is illustrated by some examples that emphasize that cooperative learning can facilitate learning, promote socialisation and increase students' self-esteem

The learning process as a cooperative task among teachers and students

This research project gathers several ideas and guidelines on professional improvement as a teacher. This study includes two empirical studies. The first one focuses mainly on the teacher's figure. It is meant to be a study of the several resources that the teacher uses in order to construct the student's knowledge in an English classroom context. The second empirical study focuses on the students. It is a study on how students learn cooperatively by analyzing their oral productions when working in small groups

How to avoid gender bias in classroom interaction? A cooperative learning experience

This project aims to analyse the kind of questions the teacher asks students in order to encourage them to participate in her classes. Consequently, the researcher has read relevant literature and has analysed a short excerpt of a video recorded during her first practicum. She has also analysed a number of activities carried out during her second practicum in order to find out if she had improved her questioning skills in the classroom