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.

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

How multiple repetitions influence the processing of self-, famous and unknown names and faces: an ERP study.

Because we live in an extremely complex social environment, people require the ability to memorize hundreds or thousands of social stimuli. The aim of this study was to investigate the effect of multiple repetitions on the processing of names and faces varying in terms of pre-experimental familiarity. We measured both behavioral and electrophysiological responses to self-, famous and unknown names and faces in three phases of the experiment (in every phase, each type of stimuli was repeated a pre-determined number of times). We found that the negative brain potential in posterior scalp sites observed approximately 170 ms after the stimulus onset (N170) was insensitive to pre-experimental familiarity but showed slight enhancement with each repetition. The negative wave in the inferior-temporal regions observed at approximately 250 ms (N250) was affected by both pre-experimental (famous>unknown) and intra-experimental familiarity (the more repetitions, the larger N250). In addition, N170 and N250 for names were larger in the left inferior-temporal region, whereas right-hemispheric or bilateral patterns of activity for faces were observed. The subsequent presentations of famous and unknown names and faces were also associated with higher amplitudes of the positive waveform in the central-parietal sites analyzed in the 320-900 ms time-window (P300). In contrast, P300 remained unchanged after the subsequent presentations of self-name and self-face. Moreover, the P300 for unknown faces grew more quickly than for unknown names. The latter suggests that the process of learning faces is more effective than learning names, possibly because faces carry more semantic information.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Capturing and Organizing Prior Student Learning with the OCW Backpack

The purpose of this paper is to present an approach for students to have non-traditional learning assessed for credit and introduce a tool that facilitates this process. The OCW Backpack system can connect self-learners with KNEXT assessment services to obtain college credit for prior learning. An ex post facto study based on historical data collected over the past two years at Kaplan University (KU) is presented to validate the portfolio assessment process. Cumulative GPA was compared for students who received experiential credit for learning derived from personal or professional experience with a matched sample of students with no experiential learning credits. The study found that students who received experiential credits perform better than the matched sample students on GPA. The findings validate the KU portfolio assessment process. Additionally, the results support the capability of the OCW Backpack to capture the critical information necessary to evaluate non-traditional learning for university credit.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Trends in self-reported past alcoholic beverage consumption and ethanol intake from 1950 to 1995 observed in eight European countries participating in the European Investigation into Cancer and Nutrition (EPIC).

OBJECTIVE To describe the trends of self-reported past consumption of alcoholic beverages and ethanol intake from 1950 to 1995 within the European Prospective Investigation into Cancer and Nutrition (EPIC). DESIGN Data on consumption of beer/cider, wine and liqueur/spirits were obtained retrospectively at age 20, 30 and 40 years to calculate average consumption and ethanol intake for the time periods 1950-1975 (at age 20), 1960-1985 (at age 30) and 1970-1995 (at age 40). Regression analysis was conducted with the time period data to assess trends in past alcoholic beverage consumption and ethanol intake with time. SETTING The EPIC project. SUBJECTS In total, 392 064 EPIC participants (275 249 women and 116 815 men) from 21 study centres in eight European countries. RESULTS Generally, increases in beer/cider consumption were observed for most EPIC centres for 1950-1975, 1960-1985 and 1970-1995. Trends in wine consumption differed according to geographical location: downward trends with time were observed for men in southern European EPIC centres, upward trends for those in middle/northern European study centres. For women, similar but less pronounced trends were observed. Because wine consumption was the major contributor to ethanol intake for both men and women in most study centres, time trends for ethanol intake showed a similar geographical pattern to that of wine consumption. CONCLUSION The different trends in alcoholic beverage consumption and ethanol intake suggest that information depicting lifetime history of ethanol intake should be included in analyses of the relationship between ethanol and chronic diseases, particularly in multi-centre studies such as EPIC.

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Self-Development of Competences for Social Inclusion Using the TENCompetence Infrastructure

This paper describes a pilot study centred on the technology-enhanced self-development of competences in lifelong learning education carried out in the challenging context of the Association of Participants Àgora. The pilot study shows that the use of the TENCompetence infrastructure, i.e. in this case the Personal Development Planner tool, provides various kinds of benefits for adult participants with low educational profiles and who are traditionally excluded from the use of innovative learning technologies and the knowledge society. The selforganized training supported by the PDP tool aims at allowing the learners to create and control their own learning plans based on their interests and educational background including informal and non-formal experiences. In this sense, the pilot participants had the opportunity to develop and improve their competences in English language (basic and advanced levels) and ICT competence profiles which are mostly related to functional and communicative skills. Besides, the use of the PDP functionalities, such as the self-assessment, the planning and the self-regulating elements allowed the participants to develop reflective skills. Pilot results also provide indications for future developments in the field of technology support for self-organized learners. The paper introduces the context and the pilot scenario, indicates the evaluation methodology applied and discusses the most significant findings derived from the pilot study.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

The art and learning patterns of knowing in nursing

Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students’ discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.