Search results for "learning."
showing 10 items of 6527 documents
Listwise Recommendation Approach with Non-negative Matrix Factorization
2018
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…
CoCoDat: a database system for organizing and selecting quantitative data on single neurons and neuronal microcircuitry.
2004
We present a novel database system for organizing and selecting quantitative experimental data on single neurons and neuronal microcircuitry that has proven useful for reference-keeping, experimental planning and computational modelling. Building on our previous experience with large neuroscientific databases, the system takes into account the diversity and method-dependence of single cell and microcircuitry data and provides tools for entering and retrieving published data without a priori interpretation or summarizing. Data representation is based on the framework suggested by biophysical theory and enables flexible combinations of data on membrane conductances, ionic and synaptic current…
Grammar is the heart of language : grammar and its role in language learning among Finnish university students
2015
This article presents and discusses views on grammar and its role in formal language learning amongst Finnish university students. The results are based on a questionnaire which was distributed to students at the University of Jyväskylä as part of institutional action research. The background to the project was a feeling amongst some teachers of increased divergence between student respectively language teacher understandings of the role of grammar in language teaching. This concern raised the need to find out how students view grammar. The knowledge about thoughts on grammar amongst students would then help teachers to adjust and adept the way grammar is used in language teaching. The main…
Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
2017
The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing be…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
2020
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)
The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects
2013
The stability-plasticity dilemma is a well-know constraint for artificial and biological neural systems. The basic idea is that learning in a parallel and distributed system requires plasticity for the integration of new knowledge, but also stability in order to prevent the forgetting of previous knowledge. Too much plasticity will result in previously encoded data being constantly forgotten, whereas too much stability will impede the efficient coding of this data at the level of the synapses. However, for the most part, neural computation has addressed the problems related to excessive plasticity or excessive stability as two different fields in the literature.
Language Learning Methodology for Adults: A Study of Linguistic Transfer
2014
Abstract The purpose of the present research is to bring together the evidence on transfer in adult L2 and L3 language acquisition and investigate the use and the relationship between languages in contact. The role of linguistic transfer ( Odlin, 1989 ) i.e. the imposition of previously learned patterns onto a new learning situation, has a facilitation or inhibition effect on the learner's progress in mastering a new language (L2 or L3). Our findings reveal that the cross-linguistic influence occurs both from the direction of the L2 to the L3 and from the L3 to the L2 ( Odlin, 2003 , Jarvis and Pavlenko, 2008 ). In the case of our participants, in the acquisition of L2 as the foreign langua…
Automation Inner Speech as an Anthropomorphic Feature Affecting Human Trust: Current Issues and Future Directions
2021
This paper aims to discuss the possible role of inner speech in influencing trust in human–automation interaction. Inner speech is an everyday covert inner monolog or dialog with oneself, which is essential for human psychological life and functioning as it is linked to self-regulation and self-awareness. Recently, in the field of machine consciousness, computational models using different forms of robot speech have been developed that make it possible to implement inner speech in robots. As is discussed, robot inner speech could be a new feature affecting human trust by increasing robot transparency and anthropomorphism.
Diversity in random subspacing ensembles
2004
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are known to quantify diversity in ensembles, but little research has been done about their appropriateness. In this paper, we compare eight measures of the ensemble diversity with regard to their correlation with the accuracy improvement due to ensembles. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the correlations for random subspacing ensembles with diffe…