multiuser detection in flat fading non gaussian channels this paper deals with the problem of multiuser detection in direct sequence code division multiple access cdma fading channels with impulsive noise this issue arises in practical situations because in many realistic wireless channels the ambient channel noise is impulsive resulting from various natural and man made impulsive sources an m estimator based structure for noncoherent demodulation of differentially phase shift keyed dpsk signals transmitted simultaneously via a cdma flat fading channel and embedded in impulsive noise is proposed and analyzed analytical and numerical results show that in highly impulsive noise the performance gain afforded by the proposed multiuser detector can be substantial when compared to the linear decorrelating detector for dpsk with little attendant increase in algorithmic complexity

drcw ovo one vs one strategy is a common and established technique in machine learning to deal with multi class classification problems it consists of dividing the original multi class problem into easier to solve binary subproblems considering each possible pair of classes since several classifiers are learned their combination becomes crucial in order to predict the class of new instances due to the division procedure a series of difficulties emerge at this stage such as the non competence problem each classifier is learned using only the instances of its corresponding pair of classes and hence it is not competent to classify instances belonging to the rest of the classes nevertheless at classification time all the outputs of the classifiers are taken into account because the competence cannot be known a priori the classification problem would be solved on this account we develop a distance based combination strategy which weights the competence of the outputs of the base classifiers depending on the closeness of the query instance to each one of the classes our aim is to reduce the effect of the non competent classifiers enhancing the results obtained by the state of the art combinations for one vs one strategy we carry out a thorough experimental study supported by the proper statistical analysis showing that the results obtained by the proposed method outperform both in terms of accuracy and kappa measures the previous combinations for one vs one strategy highlightsthe non competence is an important problem in one vs one strategy we develop a distance based combination strategy based on dynamic classifier weighting strategies weights are settled depending on the closeness of the test instance to each one of the classesthe effect of the non competent classifiers is reduced the new strategy enhances the results obtained w r t the state of the art aggregations

multicategory support vector machines two category support vector machines svm have been very popular in the machine learning community for classification problems solving multicategory problems by a series of binary classifiers is quite common in the svm paradigm however this approach may fail under various circumstances we propose the multicategory support vector machine msvm which extends the binary svm to the multicategory case and has good theoretical properties the proposed method provides a unifying framework when there are either equal or unequal misclassification costs as a tuning criterion for the msvm an approximate leave one out cross validation function called generalized approximate cross validation is derived analogous to the binary case the effectiveness of the msvm is demonstrated through the applications to cancer classification using microarray data and cloud classification with satellite radiance profiles

fuzzy zoning based classification for handwritten characters in zoning based classification a membership function defines the way a feature influences the different zones of the zoning method this paper presents a new class of membership functions which are called fuzzy membership functions fmfs for zoning based classification these fmfs can be easily adapted to the specific characteristics of a classification problem in order to maximize classification performance in this research a real coded genetic algorithm is presented to find in a single optimization procedure the optimal fmf together with the optimal zoning described by voronoi tessellation the experimental results which are carried out in the field of handwritten digit and character recognition indicate that optimal fmf performs better than other membership functions based on abstract level ranked level and measurement level weighting models which can be found in the literature

on nearest neighbor error correcting output codes with application to all pairs multiclass support vector machines a common way of constructing a multiclass classifier is by combining the outputs of several binary ones according to an error correcting output code ecoc scheme the combination is typically done via a simple nearest neighbor rule that finds the class that is closest in some sense to the outputs of the binary classifiers for these nearest neighbor ecocs we improve existing bounds on the error rate of the multiclass classifier given the average binary distance the new bounds provide insight into the one versus rest and all pairs matrices which are compared through experiments with standard datasets the results also show why elimination also known as dagsvm and hamming decoding often achieve the same accuracy

classification by pairwise coupling we discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes and then coupling the estimates together the coupling model is similar to the bradley terry method for paired comparisons we study the nature of the class probability estimates that arise and examine the performance of the procedure in real and simulated data sets classifiers used include linear discriminants nearest neighbors adaptive nonlinear methods and the support vector machine

multiuser detection in mismatched multiple access channels we consider an additive white gaussian noise multiple access channel and investigate the performance degradation of several multiuser detectors due to imperfect knowledge of the channel parameters induced by either estimation errors or sampling quantization of the receiver s sufficient statistics the symbol error rates and the asymptotic multiuser efficiencies ames of these detectors are examined as functions of both true and approximated channel parameters for 2 asynchronous users the results quantify the loss of near far resistance due to parameter estimation errors and demonstrate that detectors which are optimal for ideal conditions are often more sensitive to parameter mismatch examples are given which illustrate a reverse ordering of the symbol error rates for these detectors in the high signal to noise regime and also illustrate cases when the conventional matched filter detector exhibits superior performance over multiuser detectors

fractional order volterra equations with applications to elasticity abstract a class of volterra integro differential equations with singular kernels is studied it is chosen so that as the order of singularity approaches zero or one the equation approaches an analog of the equation for viscoelasticity or a kelvin voigt solid qualitative results are obtained and estimates given for the effect of the order of singularity on errors in galerkin s method the role of the quasi static approximation is studied

convergence of a ml parameter estimation algorithm for ds ss systems in time varying channels with strong interference an unbiased maximum likelihood ml channel parameter estimation algorithm for direct sequence spread spectrum systems with strong interference is discussed in this paper the algorithm includes correcting terms to the extended kalman filter ekf based on the gradient of the negative log likelihood function of the output of a conventional matched filter by an asymptotic analysis the algorithm is shown to determine the actual parameters a complete implementation of the algorithm is given and its transient behavior is examined by computer simulations results show the ml algorithm albeit optimal in the sense of unbiased parameter estimation is less robust than the modified ekf described in the first reference

multiuser detectors with disjoint kalman channel estimators for synchronous cdma mobile radio channels we compare performance of several multiuser detectors for differentially encoded data combined with simple disjoint decision directed kalman channel estimators over flat rayleigh fading channels simpler detectors with noncoherent differential detection are also compared different performance trends relative to the case of perfect channel estimation are observed we find that in the presence of channel mismatch the linear decorrelator is the most robust detector in terms of the bit error rate and the near far resistance parameter adjustment for fading channel modeling and estimation in the decision directed mode are also discussed

reducing multiclass to binary a unifying approach for margin classifiers we present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin based binary learning algorithm the proposed framework unifies some of the most popular approaches in which each class is compared against all others or in which all pairs of classes are compared to each other or in which output codes with error correcting properties are used we propose a general method for combining the classifiers generated on the binary problems and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms the scheme and the corresponding bounds apply to many popular classification learning algorithms including support vector machines adaboost regression logistic regression and decision tree algorithms we also give a multiclass generalization error analysis for general output codes with adaboost as the binary learner experimental results with svm and adaboost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms

linear multiuser detectors for synchronous code division multiple access channels under the assumptions of symbol synchronous transmissions and white gaussian noise the authors analyze the detection mechanism at the receiver comparing different detectors by their bit error rates in the low background noise region and by their worst case behavior in a near far environment where the received energies of the users are not necessarily similar optimum multiuser detection achieves important performance gains over conventional single user detection at the expense of computational complexity that grows exponentially with the number of users it is shown that in the synchronous case the performance achieved by linear multiuser detectors is similar to that of optimum multiuser detection attention is focused on detectors whose linear memoryless transformation is a generalized inverse of the matrix of signature waveform crosscorrelations and on the optimum linear detector it is shown that the generalized inverse detectors exhibit the same degree of near far resistance as the optimum multiuser detectors the optimum linear detector is obtained

in defense of one vs all classification we consider the problem of multiclass classification our main thesis is that a simple one vs all scheme is as accurate as any other approach assuming that the underlying binary classifiers are well tuned regularized classifiers such as support vector machines this thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification we support our position by means of a critical review of the existing literature a substantial collection of carefully controlled experimental work and theoretical arguments

why fuzzy decision trees are good rankers several fuzzy extensions of decision tree induction which is an established machine learning method have already been proposed in the literature so far however fuzzy decision trees have almost exclusively been used for the performance task of classification in this paper we show that a fuzzy extension of decision trees is arguably more useful for another performance task namely ranking roughly the goal of ranking is to order a set of instances from most likely positive to most likely negative the motivation for applying fuzzy decision trees to this problem originates from recent investigations of the ranking performance of conventional decision trees these investigations will be continued and complemented in this paper our results reveal some properties that seem to be crucial for a good ranking performance properties that are better and more naturally offered by fuzzy than by conventional decision trees most notably a fuzzy decision tree produces scores in terms of membership degrees on a fine granular scale using these membership degrees as a ranking criterion a key problem of conventional decision trees is solved in an elegant way namely the question of how to break ties between instances in the same leaf or more generally between equally scored instances

on the algorithmic implementation of multiclass kernel based vector machines in this paper we describe the algorithmic implementation of multiclass kernel based vector machines our starting point is a generalized notion of the margin to multiclass problems using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks our notion of margin yields a direct method for training multiclass predictors by using the dual of the optimization problem we are able to incorporate kernels with a compact set of constraints and decompose the dual problem into multiple optimization problems of reduced size we describe an efficient fixed point algorithm for solving the reduced optimization problems and prove its convergence we then discuss technical details that yield significant running time improvements for large datasets finally we describe various experiments with our approach comparing it to previously studied kernel based methods our experiments indicate that for multiclass problems we attain state of the art accuracy

achieving near optimum asymptotic efficiency and fading resistance over the time varying rayleigh faded cdma channel we study multiuser receiver design and analysis for synchronous code division multiple access cdma channels with time varying rayleigh fading starting from an error probability criterion we first derive a near optimum receiver for this channel that admits a detector estimator decomposition has certain asymptotic optimality properties and a complexity which is independent of the length of the observation interval the performance of this detector is analytically characterized and contrasted with that of the optimal multiuser detector for the time invariant or static cdma rayleigh fading channel when it is implemented over the time varying channel notable among our conclusions is the fact that unlike the static channel multiuser detector the time varying channel detector is able to withstand not only the estimated interference from the other system users but also the residual interference that cannot be estimated arising out of imperfect estimation of the interferer fading parameters using estimation error covariance information this detector shows flexibility in accommodating a wide range of interferer fading conditions

designing storage efficient decision trees the problem of designing storage efficient decision trees from decision tables is examined it is shown that for most cases the construction of the storage optimal decision tree is an np complete problem and therefore a heuristic approach to the problem is necessary a systematic procedure analogous to the information theoretic heuristic is developed the algorithm has low computational complexity and performs well experimentally

posterior probability based ensemble strategy using optimizing decision directed acyclic graph for multi class classification ensemble strategy is important to develop a decomposition and ensemble method for multi class classification problems most existing ensemble strategies use predetermined and heuristic decision rules in this work we build up the decision rules by optimizing decision directed acyclic graph oddag with classical and fuzzy decision trees to ensemble the posterior probabilities of binary classifiers from one vs one ovo or one vs all ova decomposition strategies for multi class classification problems four widely used extensible algorithms and ten decomposition and ensemble methods incorporating four binary classifiers bcs have been tested on 25 data sets the empirical results show that the methods based on ensemble strategies using oddag are among the top methods that achieve the best performance in terms of two different measures

fractional order evolutionary integral equations the topic of fractional calculus derivatives and integrals of arbitrary orders is enjoying growing interest not only among mathematicians but also among physicists and engineers also the evolutionary integral equations have many applications in applied mathematics in this work we are concerned with a class of evolutionary integral equations of arbitrary fractional orders the existence uniqueness and some other properties of the solutions will be proved also the continuation of the problems will be established

induction of fuzzy decision trees abstract most decision tree induction methods used for extracting knowledge in classification problems do not deal with cognitive uncertainties such as vagueness and ambiguity associated with human thinking and perception in this paper cognitive uncertainties involved in classification problems are explicitly represented measured and incorporated into the knowledge induction process a fuzzy decision tree induction method which is based on the reduction of classification ambiguity with fuzzy evidence is developed fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise conflict and missing information

isometric data sequences and data modulation schemes in fading channels in multiplicative fading channels joint channel estimation and data detection ce dd schemes cannot differentiate among certain sequences of amplitude and or phase modulated am pm symbols drawn from rotationally invariant signal constellations this paper identifies these so called isometric sequences as the main source of performance degradation and introduces a unifying framework that effectively solves the problem by using asymmetric signal constellations asc and a normalized innovations based detector the encompassing nature of the solution is clearly demonstrated by showing that seemingly unrelated previous results such as training based solutions can be viewed as special cases of the modulation based solution discussed here a comprehensive analysis supported by simulation studies of the relationships among modulation schemes isometry and detection performance is provided results indicate that the proposed asc solution offers excellent performance without incurring significant complexity or reducing the transmission rate furthermore it is shown to be robust in various fading rates and for different signal constellations

tracking of time varying channels using two step lms type adaptive algorithm this paper presents a modified version of the two step least mean square lms type adaptive algorithm motivated by the work of gazor we describe the nonstationary adaptation characteristics of this modified two step lms mg lms algorithm for the system identification problem it ensures stable behavior during convergence as well as improved tracking performance in the smoothly time varying environments the estimated weight increment vector is used for the prediction of weight vector for the next iteration the proposed modification includes the use of a control parameter to scale the estimated weight increment vector in addition to a smoothing parameter used in the two step lms g lms algorithm which controls the initial oscillatory behavior of the algorithm the analysis focuses on the effects of these parameters on the lag misadjustment in the tracking process the mathematical analysis for a nonstationary case where the plant coefficients are assumed to follow a first order markov process shows that the mg lms algorithm contributes less lag misadjustment than the conventional lms and g lms algorithms further the stability criterion imposes upper bound on the value of the control parameter these derived analytical results are verified and demonstrated with simulation examples which clearly show that the lag misadjustment reduces with increasing values of the smoothing and control parameters under permissible limits

a complete fuzzy decision tree technique in this paper a new method of fuzzy decision trees called soft decision trees sdt is presented this method combines tree growing and pruning to determine the structure of the soft decision tree with refitting and backfitting to improve its generalization capabilities the method is explained and motivated and its behavior is first analyzed empirically on 3 large databases in terms of classification error rate model complexity and cpu time a comparative study on 11 standard uci repository databases then shows that the soft decision trees produced by this method are significantly more accurate than standard decision trees moreover a global model variance study shows a much lower variance for soft decision trees than for standard trees as a direct cause of the improved accuracy

enhancing directed binary trees for multi class classification one approach to multi class classification consists in decomposing the original problem into a collection of binary classification tasks the outputs of these binary classifiers are combined to produce a single prediction winner takes all max wins and tree voting schemes are the most popular methods for this purpose however tree schemes can deliver faster predictions because they need to evaluate less binary models despite previous conclusions reported in the literature this paper shows that their performance depends on the organization of the tree scheme i e the positions where each pairwise classifier is placed on the graph different metrics are studied for this purpose proposing a new one that considers the precision and the complexity of each pairwise model what makes the method to be classifier dependent the study is performed using support vector machines svms as base classifiers but it could be extended to other kind of binary classifiers the proposed method tested on benchmark data sets and on one real world application is able to improve the accuracy of other decomposition multi class classifiers producing even faster predictions

behaviour in the large on numerical solutions to one dimensional nonlinear viscoelasticity by continuous time galerkin methods abstract we analyze the long time behaviour of fully discrete solutions to a one dimensional nonlinear viscoelastic problem it is shown that these approximations which are found by a continuous time galerkin method converge to a steady state the possible numerical steady states are characterized and in particular their high degree of dependence on initial data and mesh design is explained computational results are included which show the above dependence and indicate that the numerical solutions will typically not converge to unstable states