We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several STATISTICAL algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
The scope of this SCM simulator is to provide an easy-to-use, GUI supported, MATLAB developed application to any user who requires a practical tool to perform MIMO simulations and to obtain STATISTICAL data.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical STATISTICAL models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
driver program which uses the above two modules. I have also made available the data set pollute.dat of mortality rates against socio-economic, meteorological and pollution variables for 60 STATISTICAL areas in the USA.
The potential of solving real-time demanding industrial applications, using vision-based
algorithms, drastically grew due to an increasing availability of computational power.
In this thesis a novel real-time, vision-based Blackjack analysis system is presented. The
embedding of the vision algorithms in a compound system of other information sources such
as an electronic chip tray, reduces the vision task to detect cards and chips. Robust results
are achieved by not just analyzing single frames but an image stream regarding game-ß ow
informations. The requirements for such a system are a highly robust and adaptive behav-
ior. This is motivated by the vital interest of casino entrepreneurs in a 100 STATISTICAL
analysis of their offered gambling in order to support the business plan, measuring table
and dealer performance and give accurate player rating. Extensive experiments show the
robustness and applicability of the proposed system.
Pattern Analysis is the process of fi nding general relations in a set of data,
and forms the core of many disciplines, from neural networks to so-called syn-
tactical pattern recognition, from STATISTICAL pattern recognition to machine
learning and data mining. Applications of pattern analysis range from bioin-
formatics to document retrieval.
Features a unique program to estimate the power spectral density. The spectrum containing all significant details is calculated from a time series model. Model type as well as model order are determined automatically from the data, using STATISTICAL criteria. Robust estimation algorithms and order selection criteria are used to obtain reliable results. Unlike in FFT analysis, where the experimenter has to set the amount of smoothing of the raw FFT, the right level of detail is assessed using the data only.
During the past two decades there has been a substantial growth in research in
wireless communications. The number of journals published from various parts of
the world catering to the research community has grown exponentially. Despite
such a growth, the engineering community still needs more information so as to
thoroughly comprehend wireless channel characteristics. What specifically must be
understood are the effects of channel degradation brought on by STATISTICAL fluctua-
tions in the channel.
