Search results for " learning"

Abstraction of covariations in incidental learning and covariation bias

1997

Experiment 1 was devised to distinguish, in a given set of features composing drawn robots, those whose variations were related a priori for participants from those whose variations were a priori independent. In Expt 2, correlations were experimentally induced between a priori-related features for one group of participants (pre-primed group), and between a priori-independent features for another group {arbitrary group), in incidental learning conditions. A subsequent transfer phase revealed that participants' performances were sensitive to experimentally induced correlations in both groups. However, only the performances of the pre-primed group accurately matched the predictions of a statis…

Using the Theory of Regular Functions to Formally Prove the ε-Optimality of Discretized Pursuit Learning Algorithms

2014

Learning Automata LA can be reckoned to be the founding algorithms on which the field of Reinforcement Learning has been built. Among the families of LA, Estimator Algorithms EAs are certainly the fastest, and of these, the family of Pursuit Algorithms PAs are the pioneering work. It has recently been reported that the previous proofs for e-optimality for all the reported algorithms in the family of PAs have been flawed. We applaud the researchers who discovered this flaw, and who further proceeded to rectify the proof for the Continuous Pursuit Algorithm CPA. The latter proof, though requires the learning parameter to be continuously changing, is, to the best of our knowledge, the current …

Machine learning methods to forecast temperature in buildings

2013

Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy management is the prediction of the variables that directly affect building energy consumption and personal comfort. Among these variables, one can highlight the temperature in each room of a building. In this work we apply different machine learning techniques along with other classical ones for predicting the temperatures in different rooms. The obtained results demonstrate the validity of these techniques for predicting temperatures and, therefore, for the establishment of optim…

Contemporary Art as a Learning Experience

2012

Abstract Art has changed a lot during the last fifty years. In spite of that, the role of art in schools has remained the same. The aim of this paper is to show the many different ways contemporary art offers itself as an environment for learning. An idea of contemporary art as a learning experience presented here is based on teacher's views on the matter. The analysis of the material shows that by concretizing the properties of artworks and the experience produced by them, arguments for seeing contemporary art both as a credible and a necessary environment of learning can be found.

Transversal Skills in Dentistry - Content and Language Integrated Learning Approach

2016

Kulttuurienvälisen tietoisuuden ja kieliasenteiden kehittyminen CLIL-opetuksessa

2018

CLIL (Content and Language Integrated Learning) has been claimed to create a positive attitude towards languages and language learning in general and to raise intercultural awareness. The studies on the topics have however remained scarce. This study examines the issue from former pupils’ perspective. The 24 participants, who received English-medium CLIL during their comprehensive school in the 1990s, were interviewed in-depth and the data were analysed qualitatively. Most participants felt that CLIL had had a very positive effect on their attitudes towards English. However, many considered that CLIL had partly been detrimental to their attitudes towards and learning of other languages. The…

Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning

2021

This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the propose…

Less is More! Preliminary Evaluation of Multi-Functional Document-Based Online Learning Environment

2019

This work-in-progress paper in innovative practice category presents and evaluates a multi-functional document-based learning management system, TIM (The Interactive Material). This system is developed with the goal of integrating a rich set of features seamlessly into teachers’ every-day pedagogical and disciplinary needs. The aim is that a single system (“Less”) would provide all technological solutions necessary for online teaching and learning (“More”), hence the punchline “Less is More!” We illustrate the system and evaluate it based on feedback from teachers. This preliminary evaluation focuses on how teachers reacted to the multi-functional system and is discussed in the context of T…

Deep CNN-ELM Hybrid Models for Fire Detection in Images

2018

In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNN…

Support Vector Machines for Crop Classification Using Hyperspectral Data

2003

In this communication, we propose the use of Support Vector Machines (SVM) for crop classification using hyperspectral images. SVM are benchmarked to well–known neural networks such as multilayer perceptrons (MLP), Radial Basis Functions (RBF) and Co-Active Neural Fuzzy Inference Systems (CANFIS). Models are analyzed in terms of efficiency and robustness, which is tested according to their suitability to real–time working conditions whenever a preprocessing stage is not possible. This can be simulated by considering models with and without a preprocessing stage. Four scenarios (128, 6, 3 and 2 bands) are thus evaluated. Several conclusions are drawn: (1) SVM yield better outcomes than neura…