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- Teemu Roos, Petri Myllymäki, Henry Tirri, Pauli Misikangas, Juha Sievänen
- IJWIN
- 2002

We estimate the location of a WLAN user based on radio signal strength measurements performed by the user’s mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths… (More)

- Teemu Roos, Petri Myllymäki, Henry Tirri
- IEEE Trans. Mob. Comput.
- 2002

ÐSome location estimation methods, such as the GPS satellite navigation system, require nonstandard features either in the mobile terminal or the network. Solutions based on generic technologies not intended for location estimation purposes, such as the cell-ID method in GSM/GPRS cellular networks, are usually problematic due to their inadequate location… (More)

- Teemu Roos, Hannes Wettig, Peter Grünwald, Petri Myllymäki, Henry Tirri
- Machine Learning
- 2005

Discriminative learning of the parameters in the naive Bayes model is known to be equivalent to a logistic regression problem. Here we show that the same fact holds for much more general Bayesian network models, as long as the corresponding network structure satisfies a certain graph-theoretic property. The property holds for naive Bayes but also for more… (More)

- Teemu Roos, Petri Myllymäki, Jorma Rissanen
- IEEE Transactions on Signal Processing
- 2009

We refine and extend an earlier minimum description length (MDL) denoising criterion for wavelet-based denoising. We start by showing that the denoising problem can be reformulated as a clustering problem, where the goal is to obtain separate clusters for informative and noninformative wavelet coefficients, respectively. This suggests two refinements,… (More)

- Teemu Roos, Tuomas Heikkilä
- LLC
- 2009

Given a collection of imperfect copies of a textual document, the aim of stemmatology is to reconstruct the history of the text, indicating for each variant the sourcc tcxt from it was copied. We describe an experiment involving three artificial bcnchmark data sets to which a number of computer-assisted stemmatologr mcthods wcre applied. Contrary to earlicr… (More)

- Teemu Roos, Jorma Rissanen
- 2008

The important normalized maximum likelihood (NML) distribution is obtained via a normalization over all sequences of given length. It has two short-comings: the resulting model is usually not a random process, and in many cases, the normalizing integral or sum is hard to compute. In contrast, the recently proposed sequentially normalized maximum likelihood… (More)

- Jorma Rissanen, Teemu Roos
- 2007 Information Theory and Applications Workshop
- 2007

The NML (normalized maximum likelihood) universal model has certain minmax optimal properties but it has two shortcomings: the normalizing coefficient can be evaluated in a closed form only for special model classes, and it does not define a random process so that it cannot be used for prediction. We present a universal conditional NML model, which has… (More)

- Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllymäki
- Journal of Machine Learning Research
- 2011

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (f̂CLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient… (More)

- Tomi Silander, Teemu Roos, Petri Myllymäki
- Int. J. Approx. Reasoning
- 2010

We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background knowledge is available. The problem can be divided into two different subtasks: learning the structure of the network (a set of independence relations), and learning the parameters… (More)

In this survey-style paper we demonstrate the usefulness of the probabilistic modelling framework in solving not only the actual positioning problem, but also many related problems involving issues like calibration, active learning, error estimation and tracking with history. We also point out some interesting links between positioning research done in the… (More)