The p-norm generalization of the LMS algorithm for adaptive filtering

Recently much work has been done analyzing online machine learning algorithms in a worst case setting, where no probabilistic assumptions are made about the data. This is analogous to the H/sup /spl infin// setting used in adaptive linear filtering. Bregman divergences have become a standard tool for analyzing online machine learning algorithms. Using these divergences, we motivate a generalization of the least mean squared (LMS) algorithm. The loss bounds for these so-called p-norm algorithms involve other norms than the standard 2-norm. The bounds can be significantly better if a large proportion of the input variables are irrelevant, i.e., if the weight vector we are trying to learn is sparse. We also prove results for nonstationary targets. We only know how to apply kernel methods to the standard LMS algorithm (i.e., p=2). However, even in the general p-norm case, we can handle generalized linear models where the output of the system is a linear function combined with a nonlinear transfer function (e.g., the logistic sigmoid).     

Empirical characterization of wideband indoor radio channel at 5.3GHz

Characteristics of wideband indoor radio channel at 5.3 GHz were defined based on an extensive measurement campaign using a wideband channel sounder with 19 ns delay resolution. Pathloss exponents were 1.3-1.5 in LOS and 2.9-4.8 in non-line of sight (NLOS). Large difference in NLOS exponents was due to different dominating propagation mechanisms in different types of building structures. The delay dispersion was characterized by cumulative distribution functions (CDF) of the RMS delay spreads, the values for CDF=0.9 varied from 20 to 180 ns in different setups in an office building and large hall environments. The correlation functions of the radio channel in spatial and frequency domains were extracted. Small scale models for five typical indoor scenarios were developed using tapped delay lines     

Polarization Behaviours at 2, 5 and 60 GHz for Indoor Mobile Communications

The behaviours of linear polarizations at 2.15, 5.3 and 61.7 GHz in corridors are studied in this paper. It shows that there is no significant difference between the received powers for vertical and horizontal polarizations. Depolarization is obvious at 2.15 GHz due to different antenna type is applied at the receiver, and it is more serious in non-line-of-sight (NLOS) cases.     

Potential of amorphous Mo–Si–N films for nanoelectronic applications

The properties of amorphous metallic molybdenum–silicon–nitrogen (Mo–Si–N) films were characterised for use in nanoelectronic applications. The films were deposited by co-sputtering of molybdenum and silicon targets in a gas mixture of argon and nitrogen. The atomic composition, microstructure and surface roughness were studied by RBS, TEM and AFM analyses, respectively. The electrical properties were investigated in the temperature range 80 mK to 300 K. No transition into a superconductive state was observed. Nanoscale wires were fabricated using electron beam lithography with their properties measured as a function of temperature.     

Learning reliably and with one-sided error

Reliable and probably useful learning, proposed by Rivest and Sloan, is a variant of probably approximately correct learning. In this model the hypothesis must never misclassify an instance but is allowed to answer I don't know with a low probability. We derive upper and lower bounds for the sample complexity of reliable and probably useful learning in terms of the combinatorial characteristics of the concept class to be learned. This is done by reducing reliable and probably useful learning to learning with one-sided error. The bounds also hold for a slightly weaker model that allows the learner to output with a low probability a hypothesis that makes misclassifications. We see that in these models learning with one oracle is more difficult than learning with two oracles. Our results imply that monotone Boolean conjunctions or disjunctions cannot be learned reliably and probably usefully from a polynomial number of examples. Rectangles in ⁿ forn 2 cannot be learned from any finite number of examples.A preliminary version of this paper appeared under the title Reliable and useful learning inProceedings of the 2nd Annual Workshop on Computational Learning Theory, Morgan Kaufmann, San Mateo, CA, 1989, pp. 365–380. This work was supported by the Academy of Finland.     

Microclimate and ventilation in Estonian and Finnish dairy buildings

A series of ventilation, thermal and indoor air quality measurements were performed in 14 different dairy buildings in Estonia and Finland. The number of animals in the buildings varied from 30 to 600. Measurements were made all year round with ambient temperatures ranging between −40 °C and +30 °C. The results showed that microclimatic conditions in the dairy buildings were affected by the design of the building, outside temperature, wind, ventilation and manure handling method. The average inside air concentration of carbon dioxide was 950 ppm, ammonia 5 ppm, methane 48 ppm, relative humidity 70% and inside air velocity was 0.2 m/s. Although occasionally exceeded, the ventilation and average indoor air quality in the dairy buildings were mainly within the recommended limits.     

Performance-based testing for ICT skills assessing: a case study of students and teachers’ ICT skills in Finnish schools

Universal Access in the Information Society - Skills enabling and ensuring universal access to information have been investigated intensively during the past few years. The research results provide...     

Online learning with kernels

Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time applications. In this paper, we consider online learning in a reproducing kernel Hilbert space. By considering classical stochastic gradient descent within a feature space and the use of some straightforward tricks, we develop simple and computationally efficient algorithms for a wide range of problems such as classification, regression, and novelty detection. In addition to allowing the exploitation of the kernel trick in an online setting, we examine the value of large margins for classification in the online setting with a drifting target. We derive worst-case loss bounds, and moreover, we show the convergence of the hypothesis to the minimizer of the regularized risk functional. We present some experimental results that support the theory as well as illustrating the power of the new algorithms for online novelty detection.