52 resultados para Learning with noise
Resumo:
Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.
Resumo:
Squirmish at the Oasis takes its name from Luigi Russolo's fourth noise network 'Skirmish at the Oasis' performed in Milan in 1913. 100 years on the Agency of Noise contemplate changes in technology and the culture industry that provoke new questions around the deliberate use of noise within music and art. Through live acts of enquiry and experimentation five artists unravel paradoxes associated with the use of noise in art, music and the gallery space. The works challenge tensions, contradictions and possible oxymorons that emerge through the use and acceptance of noise within an artistic framework. Featuring: DAISY DIXON / GRAHAM DUNNING / POLLYFIBRE / DANE SUTHERLAND / MARNIE WATTS
Resumo:
We extended 'littleBits' electronic components by attaching them to a larger base that was designed to help make them easier to pick up and handle, and easier to assemble into circuits for people with learning disabilities. A pilot study with a group of students with learning disabilities was very positive. There were fewer difficulties in assembling the components into circuits, and problems such as attempting to connect them the wrong way round or the wrong way up were eliminated completely.
Resumo:
Mathematical ability is heritable, but few studies have directly investigated its molecular genetic basis. Here we aimed to identify specific genetic contributions to variation in mathematical ability. We carried out a genome wide association scan using pooled DNA in two groups of U.K. samples, based on end of secondary/high school national academic exam achievement: high (n = 419) versus low (n = 183) mathematical ability while controlling for their verbal ability. Significant differences in allele frequencies between these groups were searched for in 906,600 SNPs using the Affymetrix GeneChip Human Mapping version 6.0 array. After meeting a threshold of p<1.5×10-5, 12 SNPs from the pooled association analysis were individually genotyped in 542 of the participants and analyzed to validate the initial associations (lowest p-value 1.14 ×10-6). In this analysis, one of the SNPs (rs789859) showed significant association after Bonferroni correction, and four (rs10873824, rs4144887, rs12130910 rs2809115) were nominally significant (lowest p-value 3.278 × 10-4). Three of the SNPs of interest are located within, or near to, known genes (FAM43A, SFT2D1, C14orf64). The SNP that showed the strongest association, rs789859, is located in a region on chromosome 3q29 that has been previously linked to learning difficulties and autism. rs789859 lies 1.3 kbp downstream of LSG1, and 700 bp upstream of FAM43A, mapping within the potential promoter/regulatory region of the latter. To our knowledge, this is only the second study to investigate the association of genetic variants with mathematical ability, and it highlights a number of interesting markers for future study.
Resumo:
This project engages people with learning disabilities as co-researchers and co-designers in the development of multisensory interactive artworks, with the aim of making museums or heritage sites more interesting, meaningful, and fun. This article describes our explorations, within this context, of a range of technologies including squishy circuits, littleBits, and easy-build websites, and presents examples of objects created by the co-researchers such as “sensory boxes” and interactive buckets, baskets, and boots. Public engagement is an important part of the project and includes an annual public event and seminar day, a blog rich with photos and videos of the workshops, and an activities book to give people ideas for creating their own sensory explorations of museums and heritage sites.
Resumo:
The “littleBits go LARGE" project extends littleBits electronic modules, an existing product that is aimed at simplifying electronics for a wide range of audiences. In this project we augment the littleBits modules to make them more accessible to people with learning disabilities. We will demonstrate how we have made the modules easier to handle and manipulate physically, and how we are augmenting the design of the modules to make their functions more obvious and understandable.
Resumo:
We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.
Resumo:
The Monte Carlo Independent Column Approximation (McICA) is a flexible method for representing subgrid-scale cloud inhomogeneity in radiative transfer schemes. It does, however, introduce conditional random errors but these have been shown to have little effect on climate simulations, where spatial and temporal scales of interest are large enough for effects of noise to be averaged out. This article considers the effect of McICA noise on a numerical weather prediction (NWP) model, where the time and spatial scales of interest are much closer to those at which the errors manifest themselves; this, as we show, means that noise is more significant. We suggest methods for efficiently reducing the magnitude of McICA noise and test these methods in a global NWP version of the UK Met Office Unified Model (MetUM). The resultant errors are put into context by comparison with errors due to the widely used assumption of maximum-random-overlap of plane-parallel homogeneous cloud. For a simple implementation of the McICA scheme, forecasts of near-surface temperature are found to be worse than those obtained using the plane-parallel, maximum-random-overlap representation of clouds. However, by applying the methods suggested in this article, we can reduce noise enough to give forecasts of near-surface temperature that are an improvement on the plane-parallel maximum-random-overlap forecasts. We conclude that the McICA scheme can be used to improve the representation of clouds in NWP models, with the provision that the associated noise is sufficiently small.
Resumo:
Background There is a need to develop and adapt therapies for use with people with learning disabilities who have mental health problems. Aims To examine the performance of people with learning disabilities on two cognitive therapy tasks (emotion recognition and discrimination among thoughts, feelings and behaviours). We hypothesized that cognitive therapy task performance would be significantly correlated with IQ and receptive vocabulary, and that providing a visual cue would improve performance. Method Fifty-nine people with learning disabilities were assessed on the Wechsler Abbreviated Scale of Intelligence (WASI), the British Picture Vocabulary Scale-II (BPVS-II), a test of emotion recognition and a task requiring participants to discriminate among thoughts, feelings and behaviours. In the discrimination task, participants were randomly assigned to a visual cue condition or a no-cue condition. Results There was considerable variability in performance. Emotion recognition was significantly associated with receptive vocabulary, and discriminating among thoughts, feelings and behaviours was significantly associated with vocabulary and IQ. There was no effect of the cue on the discrimination task. Conclusion People with learning disabilities with higher IQs and good receptive vocabulary were more likely to be able to identify different emotions and to discriminate among thoughts, feelings and behaviours. This implies that they may more easily understand the cognitive model. Structured ways of simplifying the concepts used in cognitive therapy and methods of socialization and education in the cognitive model are required to aid participation of people with learning disabilities.
Resumo:
The use of Information and Communication Technology (ICT) by adults with learning disabilities has been positively promoted over the past decade. More recently, policy statements and guidance from the UK government have underlined the importance of ICT for adults with learning disabilities specifically, as well as for the population in general, through the potential it offers for social inclusion. The aim of the present study was to provide a picture of how ICT is currently being used within one organisation providing specialist services for adults with learning disabilities and more specifically to provide a picture of its use in promoting community participation. Nine day and 14 residential services were visited as part of a qualitative study to answer three main questions: What kinds of computer programs are being used? What are they being used for? Does this differ between day and residential services? Computers and digital cameras were used for a wide range of activities and ‘mainstream’ programs were used more widely than those developed for specific user groups. In day services, ICT was often embedded in wider projects and activities, whilst use in houses was based around leisure interests. In both contexts, ICT was being used to facilitate communication, although this was more linked to within-service activities, rather than those external to service provision.
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This paper reports on exploratory work investigating how children with severe and profound learning difficulties register an awareness of small quantities and how they might use this information to inform their understanding. It draws on studies of typically developing children and investigates their application to pupils whose response to conventional mathematical tasks are often limited because they lack relevance and interest. The responses of the three pupils to individualized learning contexts mirror the progression suggested in the literature, namely from awareness of number to simple actions using number cues to problem-solving behaviour
Resumo:
We argue that it is important for researchers and service providers to not only recognize the rights of children and young people with learning disabilities to have a ‘voice’, but also to work actively towards eliciting views from all. A set of guidelines for critical self-evaluation by those engaged in systematically collecting the views of children and young people with learning disabilities is proposed. The guidelines are based on a series of questions concerning: research aims and ethics (encompassing access/gatekeepers; consent/assent; confidentiality/anonymity/secrecy, recognition, feedback and ownership; and social responsibility) sampling, design and communication