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  • 1.
    Gammerman, Alexander
    et al.
    Royal Holloway Univ London, Egham, Surrey, England..
    Vovk, Vladimir
    Royal Holloway Univ London, Egham, Surrey, England..
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Carlsson, Lars
    Stena Line AB, Gothenburg, Sweden..
    Conformal and probabilistic prediction with applications: editorial2019Ingår i: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, nr 3, s. 379-380Artikel i tidskrift (Övrigt vetenskapligt)
  • 2.
    Hollmen, Jaakko
    et al.
    Aalto Univ, Dept Comp Sci, Espoo, Finland..
    Asker, Lars
    Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden..
    Karlsson, Isak
    Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden..
    Papapetrou, Panagiotis
    Stockholm Univ, Dept Comp & Syst Sci, Stockholm, Sweden..
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Wikner, Birgitta Norstedt
    Karolinska Inst, Dept Med, Ctr Pharmacoepidemiol CPE, Stockholm, Sweden..
    Ohman, Inger
    Karolinska Inst, Dept Med, Ctr Pharmacoepidemiol CPE, Stockholm, Sweden..
    Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature2018Ingår i: 11TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS (PETRA 2018), ASSOC COMPUTING MACHINERY , 2018, s. 1-4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Electronic health records contain a wealth of epidemiological information about diseases at the population level. Using a database of medical diagnoses and drug prescriptions in electronic health records, we investigate the correlation between outdoor temperature and the incidence of epistaxis over time for two groups of patients. One group consists of patients that had been diagnosed with epistaxis and also been prescribed at least one of the three anti-thrombotic agents: Warfarin, Apixaban, or Rivaroxaban. The other group consists of patients that had been diagnosed with epistaxis and not been prescribed any of the three anti-thrombotic drugs. We find a strong negative correlation between the incidence of epistaxis and outdoor temperature for the group that had not been prescribed any of the three anti-thrombotic drugs, while there is a weaker correlation between incidence of epistaxis and outdoor temperature for the other group. It is, however, clear that both groups are affected in a similar way, such that the incidence of epistaxis increases with colder temperatures.

  • 3.
    Johansson, Ulf
    et al.
    Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
    Lofstrom, Tuve
    Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
    Linusson, Henrik
    Univ Boras, Dept Informat Technol, Boras, Sweden..
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Efficient Venn predictors using random forests2019Ingår i: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, nr 3, s. 535-550Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. In addition, a probabilistic classifier must, of course, also be as accurate as possible. In this paper, Venn predictors, and its special case Venn-Abers predictors, are evaluated for probabilistic classification, using random forests as the underlying models. Venn predictors output multiple probabilities for each label, i.e., the predicted label is associated with a probability interval. Since all Venn predictors are valid in the long run, the size of the probability intervals is very important, with tighter intervals being more informative. The standard solution when calibrating a classifier is to employ an additional step, transforming the outputs from a classifier into probability estimates, using a labeled data set not employed for training of the models. For random forests, and other bagged ensembles, it is, however, possible to use the out-of-bag instances for calibration, making all training data available for both model learning and calibration. This procedure has previously been successfully applied to conformal prediction, but was here evaluated for the first time for Venn predictors. The empirical investigation, using 22 publicly available data sets, showed that all four versions of the Venn predictors were better calibrated than both the raw estimates from the random forest, and the standard techniques Platt scaling and isotonic regression. Regarding both informativeness and accuracy, the standard Venn predictor calibrated on out-of-bag instances was the best setup evaluated. Most importantly, calibrating on out-of-bag instances, instead of using a separate calibration set, resulted in tighter intervals and more accurate models on every data set, for both the Venn predictors and the Venn-Abers predictors.

  • 4.
    Karunaratne, Thashmee
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Boström, Henrik
    University of Skövde, Sweden.
    Using background knowledge for graph based learning: a case study in chemoinformatics2007Ingår i: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, HONG KONG: INT ASSOC ENGINEERS-IAENG , 2007, s. 153-157Konferensbidrag (Refereegranskat)
    Abstract [en]

    Incorporating background knowledge in the learning process is proven beneficial for numerous applications of logic based learning methods. Yet the effect of background knowledge in graph based learning is not systematically explored. This paper describes and demonstrates the first step in this direction and elaborates on how additional relevant background knowledge could be used to improve the predictive performance of a graph learner. A case study in chemoinformatics is undertaken in this regard in which various types of background knowledge are encoded in graphs that are given as input to a graph learner. It is shown that the type of background knowledge encoded indeed has an effect on the predictive performance, and it is concluded that encoding appropriate background knowledge can be more important than the choice of the graph learning algorithm.

  • 5.
    Linusson, Henrik
    et al.
    Univ Boras, Dept Informat Technol, Boras, Sweden..
    Johansson, Ulf
    Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
    Boström, Henrik
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH).
    Löfström, Tuve
    Jonkoping Univ, Dept Comp Sci & Informat, Jonkoping, Sweden..
    Classification with Reject Option Using Conformal Prediction2018Ingår i: Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I / [ed] Phung, D Tseng, VS Webb, GI Ho, B Ganji, M Rashidi, L, Springer, 2018, Vol. 10937, s. 94-105Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we propose a practically useful means of interpreting the predictions produced by a conformal classifier. The proposed interpretation leads to a classifier with a reject option, that allows the user to limit the number of erroneous predictions made on the test set, without any need to reveal the true labels of the test objects. The method described in this paper works by estimating the cumulative error count on a set of predictions provided by a conformal classifier, ordered by their confidence. Given a test set and a user-specified parameter k, the proposed classification procedure outputs the largest possible amount of predictions containing on average at most k errors, while refusing to make predictions for test objects where it is too uncertain. We conduct an empirical evaluation using benchmark datasets, and show that we are able to provide accurate estimates for the error rate on the test set.

  • 6.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS. Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Johansson, Ulf
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Löfström, Tuve
    Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik.
    On the calibration of aggregated conformal predictors2017Ingår i: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, 2017, s. 154-173Konferensbidrag (Refereegranskat)
    Abstract [en]

    Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.

  • 7. Rao, W.
    et al.
    Boström, Henrik
    KTH, Tidigare Institutioner (före 2005), Numerisk analys och datalogi, NADA.
    Xie, S.
    Rule induction for structural damage identification2004Ingår i: Proc. Int. Conf. Mach. Learning Cybernetics, 2004, s. 2865-2869Konferensbidrag (Refereegranskat)
    Abstract [en]

    Structural damage identification is becoming a worldwide research subject. Some machine learning methods have been used to solve this problem, and most of them are neural network methods. In this paper, three different rule inductive methods named as Divide-and-Conquer (DAC), Bagging and Separate-and-Conquer (SAC) are investigated for predicting the damage position and extent of a concrete beam. Then radial basis function neural network (RBFNN) is used here for comparative purposes. The rule inductive methods/ especially Bagging are shown to obtain good prediction.

  • 8.
    Safinianaini, Negar
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Kaldo, Viktor
    Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden;Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
    Gated hidden markov models for early prediction of outcome of internet-based cognitive behavioral therapy2019Ingår i: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Cham: Springer Verlag , 2019, s. 160-169Konferensbidrag (Refereegranskat)
    Abstract [en]

    Depression is a major threat to public health and its mitigation is considered to be of utmost importance. Internet-based Cognitive Behavioral Therapy (ICBT) is one of the employed treatments for depression. However, for the approach to be effective, it is crucial that the outcome of the treatment is accurately predicted as early as possible, to allow for its adaptation to the individual patient. Hidden Markov models (HMMs) have been commonly applied to characterize systematic changes in multivariate time series within health care. However, they have limited capabilities in capturing long-range interactions between emitted symbols. For the task of analyzing ICBT data, one such long-range interaction concerns the dependence of state transition on fractional change of emitted symbols. Gated Hidden Markov Models (GHMMs) are proposed as a solution to this problem. They extend standard HMMs by modifying the Expectation Maximization algorithm; for each observation sequence, the new algorithm regulates the transition probability update based on the fractional change, as specified by domain knowledge. GHMMs are compared to standard HMMs and a recently proposed approach, Inertial Hidden Markov Models, on the task of early prediction of ICBT outcome for treating depression; the algorithms are evaluated on outcome prediction, up to 7 weeks before ICBT ends. GHMMs are shown to outperform both alternative models, with an improvement of AUC ranging from 12 to 23%. These promising results indicate that considering fractional change of the observation sequence when updating state transition probabilities may indeed have a positive effect on early prediction of ICBT outcome.

  • 9.
    Vasiloudis, Theodore
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST). RISE.
    Cho, Hyunsu
    AmazonWebServices.
    Boström, Henrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
    Block-distributed Gradient Boosted Trees2019Konferensbidrag (Refereegranskat)
1 - 9 av 9
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