Search results for "Learning"

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…

Multi-Temporal Image Classification with Kernels

2009

An improved MSD-based method for PD defects classification

2006

The new proposed method of pattern recognition is based on the application of Multi-resolution Signal Decomposition (MSD) technique of wavelet transform. This technique has showed off interesting properties in capturing the embedded horizontal, vertical and diagonal variations within an image obtained from the PD pattern in a separable form. This feature was exploited to identify in the PD pattern's MSD, relative at various family of partial discharge sources, some detail images typical of a single discharge phenomenon. The classification of a generic PD phenomenon is feasible through a comparison between its detail images and the detail images typical of a single discharge phenomenon. Test…

A Support Vector Domain Description Approach to Supervised Classification of Remote Sensing Images

2007

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. T…

Graph matching for efficient classifiers adaptation

2011

In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the labeled samples in the first are projected into the second domain, thus allowing the application of any classifier in the transformed domain. Experiments on VHR series of images show the validity of the proposed method …

Conceptualising Design of Learning Management Systems to Address Institutional Realities

2018

There is growing interest in the use of E-Learning in higher educational institutions. However, studies have shown mismatches between Learning Management System (LMS) design and the general institutional context in developing countries. In this paper, we assess the design and implementation requirements for Makerere University LMS against the overall institution context. This research follows a qualitative method (interviews) and uses case study. We employ the design reality gap model to investigate the design requirements of the LMS against current institutional realities. A design reality gap of 46 was obtained implying ad-hoc measures need to be put in place otherwise the failure/stagnat…

Strategic approaches, organizational design and quality management

1998

The main contribution of this paper is to integrate into one model management and organizational fields that are normally analyzed separately: contingency factors, organizational design variables, strategic approaches and quality management approaches. The essential core of the model is constituted by three basic variables of organizational design: level of centralization, level of formalization‐standardization, and level of shared vision and common values. Through analysis using this conceptual tool, we can: assess the position of tasks and organizational units in relation to these organizational variables; evaluate the congruence between organizational variables and contingency factors; i…