Relevant bibliographies by topics / Online learning algorithms

Academic literature on the topic 'Online learning algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Online learning algorithms.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Online learning algorithms"

1

Smale, Steve, and Yuan Yao. "Online Learning Algorithms." Foundations of Computational Mathematics 6, no. 2 (September 23, 2005): 145–70. http://dx.doi.org/10.1007/s10208-004-0160-z.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xu, Chenyang, and Benjamin Moseley. "Learning-Augmented Algorithms for Online Steiner Tree." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.

Full text
Abstract:
This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.
APA, Harvard, Vancouver, ISO, and other styles
3

Ying, Yiming, and Ding-Xuan Zhou. "Online Pairwise Learning Algorithms." Neural Computation 28, no. 4 (April 2016): 743–77. http://dx.doi.org/10.1162/neco_a_00817.

Full text
Abstract:
Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.
APA, Harvard, Vancouver, ISO, and other styles
4

Le Thi, Hoai An, and Vinh Thanh Ho. "Online Learning Based on Online DCA and Application to Online Classification." Neural Computation 32, no. 4 (April 2020): 759–93. http://dx.doi.org/10.1162/neco_a_01266.

Full text
Abstract:
We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms.
APA, Harvard, Vancouver, ISO, and other styles
5

Shah, Kulin, and Naresh Manwani. "Online Active Learning of Reject Option Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5652–59. http://dx.doi.org/10.1609/aaai.v34i04.6019.

Full text
Abstract:
Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.
APA, Harvard, Vancouver, ISO, and other styles
6

Ying, Yiming, and Massimiliano Pontil. "Online Gradient Descent Learning Algorithms." Foundations of Computational Mathematics 8, no. 5 (April 25, 2007): 561–96. http://dx.doi.org/10.1007/s10208-006-0237-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

BARBAKH, WESAM, and COLIN FYFE. "ONLINE CLUSTERING ALGORITHMS." International Journal of Neural Systems 18, no. 03 (June 2008): 185–94. http://dx.doi.org/10.1142/s0129065708001518.

Full text
Abstract:
We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
APA, Harvard, Vancouver, ISO, and other styles
8

Shani, Lior, Tom Zahavy, and Shie Mannor. "Online Apprenticeship Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8240–48. http://dx.doi.org/10.1609/aaai.v36i8.20798.

Full text
Abstract:
In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms: one for policy optimization and another for learning the worst case cost. By employing optimistic exploration, we derive a convergent algorithm with O(sqrt(K)) regret, where K is the number of interactions with the MDP, and an additional linear error term that depends on the amount of expert trajectories available. Importantly, our algorithm avoids the need to solve an MDP at each iteration, making it more practical compared to prior AL methods. Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL, but where the discriminator is replaced with the costs learned by OAL. Our simulations suggest that OAL performs well in high dimensional control problems.
APA, Harvard, Vancouver, ISO, and other styles
9

Yang, Feidiao, Jiaqing Jiang, Jialin Zhang, and Xiaoming Sun. "Revisiting Online Quantum State Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6607–14. http://dx.doi.org/10.1609/aaai.v34i04.6136.

Full text
Abstract:
In this paper, we study the online quantum state learning problem which is recently proposed by Aaronson et al. (2018). In this problem, the learning algorithm sequentially predicts quantum states based on observed measurements and losses and the goal is to minimize the regret. In the previous work, the existing algorithms may output mixed quantum states. However, in many scenarios, the prediction of a pure quantum state is required. In this paper, we first propose a Follow-the-Perturbed-Leader (FTPL) algorithm that can guarantee to predict pure quantum states. Theoretical analysis shows that our algorithm can achieve an O(√T) expected regret under some reasonable settings. In the case that the pure state prediction is not mandatory, we propose another deterministic learning algorithm which is simpler and more efficient. The algorithm is based on the online gradient descent (OGD) method and can also achieve an O(√T) regret bound. The main technical contribution of this result is an algorithm of projecting an arbitrary Hermitian matrix onto the set of density matrices with respect to the Frobenius norm. We think this subroutine is of independent interest and can be widely used in many other problems in the quantum computing area. In addition to the theoretical analysis, we evaluate the algorithms with a series of simulation experiments. The experimental results show that our FTPL method and OGD method outperform the existing RFTL approach proposed by Aaronson et al. (2018) in almost all settings. In the implementation of the RFTL approach, we give a closed-form solution to the algorithm. This provides an efficient, accurate, and completely executable solution to the RFTL method.
APA, Harvard, Vancouver, ISO, and other styles
10

Du, Bingqian, Zhiyi Huang, and Chuan Wu. "Adversarial Deep Learning for Online Resource Allocation." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 4 (December 31, 2021): 1–25. http://dx.doi.org/10.1145/3494526.

Full text
Abstract:
Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Online learning algorithms"

1

Harrington, Edward Francis. "Aspects of online learning /." View thesis entry in Australian Digital Theses Program, 2004. http://thesis.anu.edu.au/public/adt-ANU20060328.160810/index.html.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Harrington, Edward, and edwardharrington@homemail com au. "Aspects of Online Learning." The Australian National University. Research School of Information Sciences and Engineering, 2004. http://thesis.anu.edu.au./public/adt-ANU20060328.160810.

Full text
Abstract:
Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.
APA, Harvard, Vancouver, ISO, and other styles
3

Pasteris, S. U. "Efficient algorithms for online learning over graphs." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1516210/.

Full text
Abstract:
In this thesis we consider the problem of online learning with labelled graphs, in particular designing algorithms that can perform this problem quickly and with low memory requirements. We consider the tasks of Classification (in which we are asked to predict the labels of vertices) and Similarity Prediction (in which we are asked to predict whether two given vertices have the same label). The first half of the thesis considers non- probabilistic online learning, where there is no probability distribution on the labelling and we bound the number of mistakes of an algorithm by a function of the labelling's complexity (i.e. its "naturalness"), often the cut- size. The second half of the thesis considers probabilistic machine learning in which we have a known probability distribution on the labelling. Before considering probabilistic online learning we first analyse the junction tree algorithm, on which we base our online algorithms, and design a new ver- sion of it, superior to the otherwise current state of the art. Explicitly, the novel contributions of this thesis are as follows: • A new algorithm for online prediction of the labelling of a graph which has better performance than previous algorithms on certain graph and labelling families. • Two algorithms for online similarity prediction on a graph (a novel problem solved in this thesis). One performs very well whilst the other not so well but which runs exponentially faster. • A new (better than before, in terms of time and space complexity) state of the art junction tree algorithm, as well as an application of it to the problem of online learning in an Ising model. • An algorithm that, in linear time, finds the optimal junction tree for online inference in tree-structured Ising models, the resulting online junction tree algorithm being far superior to the previous state of the art. All claims in this thesis are supported by mathematical proofs.
APA, Harvard, Vancouver, ISO, and other styles
4

Packer, Heather S. "Evolving ontologies with online learning and forgetting algorithms." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/194923/.

Full text
Abstract:
Agents that require vocabularies to complete tasks can be limited by static vocabularies which cannot evolve to meet unforeseen domain tasks, or reflect its changing needs or environment. However, agents can benefit from using evolution algorithms to evolve their vocabularies, namely the ability to support new domain tasks. While an agent can capitalise on being able support more domain tasks, using existing techniques can hinder them because they do not consider the associated costs involved with evolving an agent's ontology. With this motivation, we explore the area of ontology evolution in agent systems, and focus on the reduction of the costs associated with an evolving ontology. In more detail, we consider how an agent can reduce the costs of evolving an ontology, these include costs associated with: the acquisition of new concepts; processing new concepts; the increased memory usage from storing new concepts; and the removal of unnecessary concepts. Previous work reported in the literature has largely failed to analyse these costs in the context of evolving an agent's ontology. Against this background, we investigate and develop algorithms to enable agents to evolve their ontologies. More specifically, we present three online evolution algorithms that enable agents to: i) augment domain related concepts, ii) use prediction to select concepts to learn, and iii) prune unnecessary concepts from their ontology, with the aim to reduce the costs associated with the acquisition, processing and storage of acquired concepts. In order to evaluate our evolution algorithms, we developed an agent framework which enables agents to use these algorithms and measure an agent's performance. Finally, our empirical evaluation shows that our algorithms are successful in reducing the costs associated with evolving an agent's ontology.
APA, Harvard, Vancouver, ISO, and other styles
5

Li, Le. "Online stochastic algorithms." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0031.

Full text
Abstract:
Cette thèse travaille principalement sur trois sujets. Le premier concentre sur le clustering en ligne dans lequel nous présentons un nouvel algorithme stochastique adaptatif pour regrouper des ensembles de données en ligne. Cet algorithme repose sur l'approche quasi-bayésienne, avec une estimation dynamique (i.e., dépendant du temps) du nombre de clusters. Nous prouvons que cet algorithme atteint une borne de regret de l'ordre et que cette borne est asymptotiquement minimax sous la contrainte sur le nombre de clusters. Nous proposons aussi une implémentation par RJMCMC. Le deuxième sujet est lié à l'apprentissage séquentiel des courbes principales qui cherche à résumer une séquence des données par une courbe continue. Pour ce faire, nous présentons une procédure basée sur une approche maximum a posteriori pour le quasi-posteriori de Gibbs. Nous montrons que la borne de regret de cet algorithme et celui de sa version adaptative est sous-linéaire en l'horizon temporel T. En outre, nous proposons une implémentation par un algorithme glouton local qui intègre des éléments de sleeping experts et de bandit à plusieurs bras. Le troisième concerne les travaux qui visent à accomplir des tâches pratiques au sein d'iAdvize, l'entreprise qui soutient cette thèse. Il inclut l'analyse des sentiments pour les messages textuels et l'implémentation de chatbot dans lesquels la première est réalisé par les méthodes classiques dans la fouille de textes et les statistiques et la seconde repose sur le traitement du langage naturel et les réseaux de neurones artificiels
This thesis works mainly on three subjects. The first one is online clustering in which we introduce a new and adaptive stochastic algorithm to cluster online dataset. It relies on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that this algorithm has a regret bound of the order of and is asymptotically minimax under the constraint on the number of clusters. A RJMCMC-flavored implementation is also proposed. The second subject is related to the sequential learning of principal curves which seeks to represent a sequence of data by a continuous polygonal curve. To this aim, we introduce a procedure based on the MAP of Gibbs-posterior that can give polygonal lines whose number of segments can be chosen automatically. We also show that our procedure is supported by regret bounds with sublinear remainder terms. In addition, a greedy local search implementation that incorporates both sleeping experts and multi-armed bandit ingredients is presented. The third one concerns about the work which aims to fulfilling practical tasks within iAdvize, the company which supports this thesis. It includes sentiment analysis for textual messages by using methods in both text mining and statistics, and implementation of chatbot based on nature language processing and neural networks
APA, Harvard, Vancouver, ISO, and other styles
6

Minerva, Michela. "Automated Configuration of Offline/Online Algorithms: an Empirical Model Learning Approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22649/.

Full text
Abstract:
The energy management system is the intelligent core of a virtual power plant and it manages power flows among units in the grid. This implies dealing with optimization under uncertainty because entities such as loads and renewable energy resources have stochastic behaviors. A hybrid offline/online optimization technique can be applied in such problems to ensure efficient online computation. This work devises an approach that integrates machine learning and optimization models to perform automatic algorithm configuration. It is inserted as the top component in a two-level hierarchical optimization system for the VPP, with the goal of configuring the low-level offline/online optimizer. Data from the low-level algorithm is used for training machine learning models - decision trees and neural networks – that capture the highly complex behavior of both the controlled VPP and the offline/online optimizer. Then, Empirical Model Learning is adopted to build the optimization problem, integrating usual mathematical programming and ML models. The proposed approach successfully combines optimization and machine learning in a data-driven and flexible tool that performs automatic configuration and forecasting of the low-level algorithm for unseen input instances.
APA, Harvard, Vancouver, ISO, and other styles
7

Pesaranghader, Ali. "A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38190.

Full text
Abstract:
Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding’s inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid’s inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.
APA, Harvard, Vancouver, ISO, and other styles
8

Al-Janabi, Mohammed Fadhil Zamil. "Detection of suspicious URLs in online social networks using supervised machine learning algorithms." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5581/.

Full text
Abstract:
This thesis proposes the use of several supervised machine learning classification models that were built to detect the distribution of malicious content in OSNs. The main focus was on ensemble learning algorithms such as Random Forest, gradient boosting trees, extra trees, and XGBoost. Features were used to identify social network posts that contain malicious URLs derived from several sources, such as domain WHOIS record, web page content, URL lexical and redirection data, and Twitter metadata. The thesis describes a systematic analysis of the hyper-parameters of tree-based models. The impact of key parameters, such as the number of trees, depth of trees and minimum size of leaf nodes on classification performance, was assessed. The results show that controlling the complexity of Random Forest classifiers applied to social media spam is essential to avoid overfitting and optimise performance. The model complexity could be reduced by removing uninformative features, as the complexity they add to the model is greater than the advantages they give to the model to make decisions. Moreover, model-combining methods were tested, which are the voting and stacking methods. Both show advantages and disadvantages; however, in general, they appear to provide a statistically significant improvement in comparison to the highest singular model. The critical benefit of applying the stacking method to automate the model selection process is that it is effective in giving more weight to more topperforming models and less affected by weak ones. Finally, 'SuspectRate', an online malicious URL detection system, was built to offer a service to give a suspicious probability of tweets with attached URLs. A key feature of this system is that it can dynamically retrain and expand current models.
APA, Harvard, Vancouver, ISO, and other styles
9

Zheng, Zhilin. "Learning Group Composition and Re-composition in Large-scale Online Learning Contexts." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18412.

Full text
Abstract:
Die Erforschung der Zusammenstellung kleiner Lerngruppen beschäftigt sich mit dem Problem, eine passende Gruppenzusammensetzung in einer Population von Lernern zu finden, die jeder Gruppe optimalen Nutzen bringen könnte. In letzter Zeit sind viele Studien zu diesem Problem der Kleingruppenzusammenstellung durchgeführt worden. Allerdings waren diese Forschungen nur selten auf den Kontext großer Lerner-Populationen ausgerichtet. Angesichts des zunehmenden Aufkommens von MOOCs muss jedoch das Problem der Gruppenzusammenstellung entsprechend erweitert betrachtet werden, und zwar mit neuen Forschungen, die den Kontext derartig großer Lerner-Populationen berücksichtigen. Anders als in Klassenzimmer-Settings könnte die beobachtete hohe Abbruchquote in MOOCs in einer Unterbesetzung der Gruppengröße resultieren und könnte somit viele Lerner dazu bringen, neue Gruppen zu bilden. Zusätzlich zur Gruppenzusammenstellung muss daher die Gruppenneuzusammenstellung als neues Thema in aktuellen Kontexten großer Lerner-Populationen ebenfalls erforscht werden. Die Untersuchungen der vorliegenden Arbeit gliedern sich in zwei Teile. Der erste Teil beschäftigt sich mit Gruppenzusammenstellung. In diesem Teil stelle ich einen diskreten-PSO Algorithmus zur Zusammenstellung kleiner Lerngruppen vor und vergleiche bislang bestehende Gruppenzusammenstellungs-Algorithmen unter den Gesichtspunkten Zeitaufwand und Gruppierungsqualität. Um Gruppenzusammenstellung in MOOCs anzuwenden wurde ein Gruppenzusammenstellungsexperiment in einem MOOC durchgeführt. Die Hauptergebnisse deuten darauf hin, dass die Gruppenzusammenstellung die Abbruchsquote reduzieren kann, jedoch lediglich einen sehr schwachen Bezug zur Lernperformanz der Lerner aufweist. Der zweite Teil beschäftigt sich mit Gruppenneuzusammenstellung. Die vorliegende Arbeit stellt eine datengesteuerte Herangehensweise vor, die umfassenden Gebrauch von Gruppeninteraktionsdaten macht sowie Gruppendynamik mit einbezieht. Mittels einer in einem Simulationsexperiment durchgeführten Evaluation zeigen sich die Vorteile dieses Verfahrens: Der Lerngruppenzusammenhalt wird verbessert und die Abbruchsquote im Vergleich zu einer Zufallsverteilung reduziert. Darüberhinaus wurde hier ein Gruppen-Lern-Werkzeug entwickelt und für die Praxis vorbereitet, das die Anforderungen des geforderten Ansatzes der Gruppenneuzusammenstellung erfüllt.
Small learning group composition addresses the problem of seeking such matching among a population of students that it could bring each group optimal benefits. Recently, many studies have been conducted to address this small group composition problem. Nevertheless, the focus of such a body of research has rarely been cast to large-scale contexts. Due to the recent come of MOOCs, the topic of group composition needs to be accordingly extended with new investigations in such large learning contexts. Different from classroom settings, the reported high drop-out rate of MOOCs could result in group’s incompletion in size and thus might compel many students to compose new groups. Thus, in addition to group composition, group re-composition as a new topic needs to be studied in current large-scale learning contexts as well. In this thesis, the research is structured in two stages. The first stage is group composition. In this part, I proposed a discrete-PSO algorithm to compose small learning groups and compared the existing group composition algorithms from the perspectives of time cost and grouping quality. To implement group composition in MOOCs, a group composition experiment was conducted in a MOOC. The main results indicate that group composition can reduce drop-out rate, yet has a very weak association with students’ learning performance. The second stage is to cope with group re-composition. This thesis suggests a data-driven approach that makes full use of group interaction data and accounts for group dynamics. Through evaluation in a simulation experiment, it shows its advantages of bringing us more cohesive learning groups and reducing the drop-out rate compared to a random condition. Apart from these, a group learning tool that fulfills the goals of the proposed group re-composition approach has been developed and is made ready for practice.
APA, Harvard, Vancouver, ISO, and other styles
10

Heidari, Fariba. "Quality of service routing using decentralized learning." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115672.

Full text
Abstract:
This thesis presents several decentralized, learning-based algorithms for on-line routing of bandwidth guaranteed paths. The presented routing algorithms do not need any a-priori knowledge of traffic demand; they use only their locally observed events and update their routing policy using learning schemes. The employed learning algorithms are either learning automata or the multi-armed bandit algorithms. We investigate the asymptotic behavior of the proposed routing algorithms and prove the convergence of one of them to the user equilibrium. Discrete event simulation results show the merit of these algorithms in terms of improving the resource utilization and increasing the network admissibility compared with shortest path routing.
We investigate the performance degradation due to decentralized routing as opposed to centralized optimal routing policies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We provide nonlinear programming formulations of these problems along with a distributed recursive approach to compute the solutions. An on-line partially-decentralized control architecture is also proposed to achieve the system optimal and the Nash bargaining solution performances. Numerical results in some practical scenarios with well engineered networks, where the network resources and traffic demand are well matched, indicate that decentralized learning techniques provide efficient, stable and scalable approaches for routing the bandwidth guaranteed paths.
In the context of on-line learning, we propose a new algorithm to track the best action-selection policy when it abruptly changes over time. The proposed algorithm employs change detection mechanisms to detect the sudden changes and restarts the learning process on the detection of an abrupt change. The performance analysis of this study reveals that when all the changes are detectable by the change detection mechanism, the proposed tracking the best action-selection policy algorithm is rate optimal. On-line routing of bandwidth guaranteed paths with the potential occurrence of network shocks such as significant changes in the traffic demand is one of the applications of the devised algorithm. Simulation results show the merit of the proposed algorithm in tracking the optimal routing policy when it abruptly changes.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Online learning algorithms"

1

Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Beer, David. Social Power of Algorithms. Taylor & Francis Group, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Mehta, Vaishali, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, and Sergio Márquez Sánchez, eds. Challenges and Opportunities for Deep Learning Applications in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150360601220101.

Full text
Abstract:
The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.
APA, Harvard, Vancouver, ISO, and other styles
9

Martin, Emily. Experiments of the Mind. Princeton University Press, 2022. http://dx.doi.org/10.23943/princeton/9780691230719.001.0001.

Full text
Abstract:
Experimental cognitive psychology research is a hidden force in our online lives. We engage with it, often unknowingly, whenever we download a health app, complete a Facebook quiz, or rate our latest purchase. How did experimental psychology come to play an outsized role in these developments? This book considers this question through a look at cognitive psychology laboratories. The book traces how psychological research methods evolved, escaped the boundaries of the discipline, and infiltrated social media and our digital universe. The book’s author recounts her participation in psychology labs, and she conveys their activities through the voices of principal investigators, graduate students, and subjects. Despite claims of experimental psychology’s focus on isolated individuals, the author finds that the history of the field—from early German labs to Gestalt psychology—has led to research methods that are, in fact, highly social. The author shows how these methods are deployed online: amplified by troves of data and powerful machine learning, an unprecedented model of human psychology is now widespread—one in which statistical measures are paired with algorithms to predict and influence users’ behavior. The book examines how psychology research has shaped us to be perfectly suited for our networked age.
APA, Harvard, Vancouver, ISO, and other styles
10

van, José. Education. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190889760.003.0007.

Full text
Abstract:
This chapter investigates how platformization is affecting the idea of education as a common good on both sides of the Atlantic. The growth of online educational platforms has been explosive, in both primary and higher education. Most of these educational platforms are corporately owned, propelled by algorithmic architectures and business models. They have quickly gained millions of users and are altering learning processes and teaching practices; they boost the distribution of online course material, hence impacting curriculums; they influence the administration of schools and universities; and, as some argue, they change the governance of (public) education as a whole. The chapter explores how, powered by the Big Five, these educational platforms are pushing a new concept of learning that questions values that are fundamental to publicly funded education: Bildung, a knowledge-based curriculum, autonomy for teachers, collective affordability, and education as a vehicle for socioeconomic equality.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Online learning algorithms"

1

Kao, Ming-Yang. "Online Learning." In Encyclopedia of Algorithms, 598. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_265.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Blum, Avrim. "On-Line Algorithms in Machine Learning." In Online Algorithms, 306–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029575.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yu, Yaoliang. "Online Learning and Optimization." In Encyclopedia of Algorithms, 1443–48. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_265.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yu, Yaoliang. "Online Learning and Optimization." In Encyclopedia of Algorithms, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_265-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ullman, Jonathan. "Query Release via Online Learning." In Encyclopedia of Algorithms, 1716–19. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_551.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ullman, Jonathan. "Query Release via Online Learning." In Encyclopedia of Algorithms, 1–5. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_551-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Marbán, Sebastián, Cyriel Rutten, and Tjark Vredeveld. "Learning in Stochastic Machine Scheduling." In Approximation and Online Algorithms, 21–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29116-6_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hazan, Elad, Adam Kalai, Satyen Kale, and Amit Agarwal. "Logarithmic Regret Algorithms for Online Convex Optimization." In Learning Theory, 499–513. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kalai, Adam, and Santosh Vempala. "Efficient Algorithms for Online Decision Problems." In Learning Theory and Kernel Machines, 26–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Barbakh, Wesam Ashour, Ying Wu, and Colin Fyfe. "Online Clustering Algorithms and Reinforcement Learning." In Non-Standard Parameter Adaptation for Exploratory Data Analysis, 85–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04005-4_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Online learning algorithms"

1

Hao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi, and Chunyan Miao. "Online Multitask Relative Similarity Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/253.

Full text
Abstract:
Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.
APA, Harvard, Vancouver, ISO, and other styles
2

Chiang, Chao-Kai, and Chi-Jen Lu. "Online Learning with Queries." In Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611973075.52.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kuh, Anthony, Muhammad Sharif Uddin, and Phyllis Ng. "Online unsupervised kernel learning algorithms." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282179.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhao, Shu. "Semi-online Algorithms on Two Hierarchical Machines with Reassignment." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00129.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sharma, Himanshu, and Satbir Jain. "Online Learning Algorithms for Dynamic Scheduling Problems." In 2011 Second International Conference on Emerging Applications of Information Technology (EAIT). IEEE, 2011. http://dx.doi.org/10.1109/eait.2011.40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gai, Yi, and Bhaskar Krishnamachari. "Online learning algorithms for stochastic water-filling." In 2012 Information Theory and Applications Workshop (ITA). IEEE, 2012. http://dx.doi.org/10.1109/ita.2012.6181777.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Jialei, Ji Wan, Yongdong Zhang, and Steven Hoi. "SOLAR: Scalable Online Learning Algorithms for Ranking." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-1163.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Dang, Minh Chuong, and Duc Dung Nguyen. "Attention mechanics for improving online Multi-Object Tracking." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00040.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Yang, Peng, Peilin Zhao, and Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.

Full text
Abstract:
Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full information label is provided for each node, which is violated in many practical applications where labeling is expensive and the oracle may only tell whether the prediction is correct or not. To address this issue, we propose a bandit online algorithm on graphs. It derives per-instance confidence region of the prediction, from which the model can be learned adaptively to minimize the online regret. Experiments on benchmark graph datasets show that the proposed bandit algorithm outperforms state-of-the-art competitors, even sometimes beats the algorithms using full information label feedback.
APA, Harvard, Vancouver, ISO, and other styles
10

Jameel, Mohd Yusuf, Mason Stahl, Jaclyn Gehring, and Denis Valle. "NOVEL HYDROGEOLOGICAL PREDICTIONS AND INFERENCES USING MACHINE LEARNING ALGORITHMS: THREE ILLUSTRATIVE EXAMPLES." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-354852.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Online learning algorithms' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography