292,182 research outputs found
Insurance: an R-Program to Model Insurance Data
Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and [epsilon]-support vector regression from modern statistical machine learning. --Claim size,insurance tariff,logistic regression,statistical machine learning,support vector regression
A support vector-based interval type-2 fuzzy system
In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines are similar to fuzzy systems in that they can also model systems that are non-linear in nature. In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and an efficient closed-form type reduction strategy is used to simplify the computations. Support vector regression improved the generalisation performance of the fuzzy rule-based system in which the fuzzy rules were a set of interpretable IF-THEN rules. The performance of the proposed model was demonstrated by conducting case studies for the non-linear system approximation and prediction of chaotic time series. The model yielded promising results and the simulation results are compared to the results published in the area
A support vector-based interval type-2 fuzzy system
In this paper, a new fuzzy regression model that is supported by support vector regression is presented. Type-2 fuzzy systems are able to tackle applications that have significant uncertainty. However general type-2 fuzzy systems are more complex than type-1 fuzzy systems. Support vector machines are similar to fuzzy systems in that they can also model systems that are non-linear in nature. In the proposed model the consequent parameters of type-2 fuzzy rules are learnt using support vector regression and an efficient closed-form type reduction strategy is used to simplify the computations. Support vector regression improved the generalisation performance of the fuzzy rule-based system in which the fuzzy rules were a set of interpretable IF-THEN rules. The performance of the proposed model was demonstrated by conducting case studies for the non-linear system approximation and prediction of chaotic time series. The model yielded promising results and the simulation results are compared to the results published in the area
Supervised Machine Learning for Signals Having RRC Shaped Pulses
Classification performances of the supervised machine learning techniques
such as support vector machines, neural networks and logistic regression are
compared for modulation recognition purposes. The simple and robust features
are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
having root-raised-cosine shaped pulses are simulated in extreme noisy
conditions having joint impurities of block fading, lack of symbol and sampling
synchronization, carrier offset, and additive white Gaussian noise. The
features are based on sample mean and sample variance of the imaginary part of
the product of two consecutive complex signal values.Comment: 5 page
Predicting the dissolution kinetics of silicate glasses using machine learning
Predicting the dissolution rates of silicate glasses in aqueous conditions is
a complex task as the underlying mechanism(s) remain poorly understood and the
dissolution kinetics can depend on a large number of intrinsic and extrinsic
factors. Here, we assess the potential of data-driven models based on machine
learning to predict the dissolution rates of various aluminosilicate glasses
exposed to a wide range of solution pH values, from acidic to caustic
conditions. Four classes of machine learning methods are investigated, namely,
linear regression, support vector machine regression, random forest, and
artificial neural network. We observe that, although linear methods all fail to
describe the dissolution kinetics, the artificial neural network approach
offers excellent predictions, thanks to its inherent ability to handle
non-linear data. Overall, we suggest that a more extensive use of machine
learning approaches could significantly accelerate the design of novel glasses
with tailored properties
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