Search results for "Learning"
showing 10 items of 6669 documents
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
2014
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about "what-is-happening-now" with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that…
Sequential Learning with LS-SVM for Large-Scale Data Sets
2006
We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We improve upon existing approaches (esp. the kernel recursive least squares algorithm) by proposing a new, supervised criterion for the selection of the relevant basis functions that takes into account the approximation error incurred from approximating the kernel as well as the reduction of the cost in th…
Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis
2006
Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…
Mašīnmācīšanās uzdevumu risināšanai interaktīvās tekstuālās vidēs
2021
Interaktīvas tekstuālas piedzīvojumu spēles var izmantot, lai pārbaudītu mašīnmācīšanās aģentu spējas tikt galā ar dažādiem izaicinājumiem, kas saistīti ar dabiskās valodas izpratni, problēmu risināšanu un atbilžu meklēšanu, vai tādas darbības izvēles stratēģiju apgūšana, kas vispārinās uz iepriekš nesastaptām vidēm. TextWorld platforma ir šādiem pētījumiem domāts ietvars un palīgrīki, ar kuru palīdzību var darbināt daudzas iepriekšpublicētas teksta piedzīvojumu spēles, vai arī definēt un ģenerēt jaunas spēles, dažādās sarežģītības pakāpēs un gandrīz bezgalīgās variācijās. Šajā darbā aprakstīta tāda algoritmiska orākula (oracle) ieviešana, kas var veiksmīgi atrisināt spēles no 3 dažādām iep…
POMDP problēmu risināšana, izmantojot vēsturiskus elementus, un tās optimizācija
2017
Elvja Egles Bakalaura darba „POMDP problēmu risināšana, izmantojot vēsturiskus elementus, un tās optimizācija” ietvaros tika izpētīti daļēji novērojami Markova lēmuma procesu (POMDP) algoritmi. Balstoties uz Jāņa Zutera zinātniski pētniecisko raksta saturu „Sequence Q-Learning: a Memory-based Method Towards Solving POMDP”, kurā ir aprakstīta POMDP risināšanas ideja, tika izstrādāts līdzvērtīgs mašīnmācīšanās algoritms, saskaņā ar rakstā sniegto informāciju. Bakalaura darba ietvaros tika pētīta šī Algoritma efektivitāte, kā arī apskatītas un piedāvātas vairākas iespējas tā tālākai pilnveidei. Programmētais darbs tika pievērsts konkrētai problēmai, kurai bija novērojama labāka ātrdarbība ar n…
The effects of associative and semantic priming in the lexical decision task.
2001
Four lexical decision experiments were conducted to examine under which conditions automatic semantic priming effects can be obtained. Experiments 1 and 2 analyzed associative/semantic effects at several very short stimulus-onset asynchronies (SOAs), whereas Experiments 3 and 4 used a single-presentation paradigm at two response-stimulus intervals (RSIs). Experiment 1 tested associatively related pairs from three semantic categories (synonyms, antonyms, and category coordinates). The results showed reliable associative priming effects at all SOAs. In addition, the correlation between associative strength and magnitude of priming was significant only at the shortest SOA (66 ms). When prime-t…
Early stages of the acute physical stress response increase loss aversion and learning on decision making: A Bayesian approach
2021
Abstract When the cortisol peak is reached after a stressor people learn slower and make worse decisions in the Iowa Gambling Task (IGT). However, the effects of the early stress response have not received as much attention. Since physical exercise is an important neuroendocrine stressor, this study aimed to fill this gap using an acute physical stressor. We hypothesized that this stress stage would promote an alertness that may increase feedback-sensitivity and, therefore, reward-learning during IGT, leading to a greater overall decision-making. 90 participants were divided into two groups: 47 were exposed to an acute intense physical stressor (cycloergometer) and 43 to a distractor 5 min …
Not a Target. A Deep Learning Approach for a Warning and Decision Support System to Improve Safety and Security of Humanitarian Aid Workers
2019
Humanitarian aid workers who try to provide aid to the most vulnerable populations in the Middle East or Africa are risking their own lives and safety to help others. The current lack of a collaborative real-time information system to predict threats prevents responders and local partners from developing a shared understanding of potentially threatening situations, causing increased response times and leading to inadequate protection. To solve this problem, this paper presents a threat detection and decision support system that combines knowledge and information from a network of responders with automated and modular threat detection. The system consists of three parts. It first collects te…
A dynamic integration algorithm for an ensemble of classifiers
1999
Numerous data mining methods have recently been developed, and there is often a need to select the most appropriate data mining method or methods. The method selection can be done statically or dynamically. Dynamic selection takes into account characteristics of a new instance and usually results in higher classification accuracy. We discuss a dynamic integration algorithm for an ensemble of classifiers. Our algorithm is a new variation of the stacked generalization method and is based on the basic assumption that each basic classifier is best inside certain subareas of the application domain. The algorithm includes two main phases: a learning phase, which collects information about the qua…
Verbal ordinal classification with multicriteria decision aiding
2008
Abstract Professionals in neuropsychology usually perform diagnoses of patients’ behaviour in a verbal rather than in a numerical form. This fact generates interest in decision support systems that process verbal data. It also motivates us to develop methods for the classification of such data. In this paper, we describe ways of aiding classification of a discrete set of objects, evaluated on set of criteria that may have verbal estimations, into ordered decision classes. In some situations, there is no explicit additional information available, while in others it is possible to order the criteria lexicographically. We consider both of these cases. The proposed Dichotomic Classification (DC…