extended nearest neighbor chain induced instance weights for svms prior knowledge plays an important role in increasing the performance of support vector machine svm one way to utilize prior knowledge is to assign different weight to each sample which is called weighted support vector machine wsvm in this paper a novel instance weighted method is proposed for support vector machines the weight is determined by the distances between a sequence of the nearest heterogeneous samples named the extended nearest neighbor chain the samples near the decision plane also locate near the heterogeneous samples thus the samples near the decision plane can be found by the extended nearest neighbor chain before svms training and then assigned higher weights meanwhile the samples far away from the decision plane are assigned lower weights this is consistent with the fact that the samples near the decision plane are more important for svms moreover this strategy can also assign lower weights to the samples which locate in the wrong classes the results of the experiments performed on both artificial synthetic datasets and real world benchmark datasets demonstrate that the proposed instance weighted method can further improve the performance of svms a novel instance weighted strategy is proposed for svms the samples near decision plane are assigned higher weights via the proposed method the sample near decision plane can be found by a chain named extended nearest neighbor chain the proposed method makes svm become more robust for noises

on 2 d pole placement the problem of characteristic polynomial assignment by dynamic output feedback in 2 d systems is solved via 2 d polynomial methods a simple necessary and sufficient condition is given the class of all assignable characteristic polynomials is described in a parametric form stable and causal solutions are treated

sparse ensembles using weighted combination methods based on linear programming an ensemble of multiple classifiers is widely considered to be an effective technique for improving accuracy and stability of a single classifier this paper proposes a framework of sparse ensembles and deals with new linear weighted combination methods for sparse ensembles sparse ensemble is to sparsely combine the outputs of multiple classifiers by using a sparse weight vector when the continuous outputs of multiple classifiers are provided in our methods the problem of solving sparse weight vector can be formulated as linear programming problems in which the hinge loss or and the 1 norm regularization are exploited both the hinge loss and the 1 norm regularization are techniques inducing sparsity used in machine learning we only ensemble classifiers with nonzero weight coefficients in these lp based methods the ensemble training error is minimized while the weight vector of ensemble learning is controlled which can be thought as implementing the structure risk minimization rule and naturally explains good performance of these methods the promising experimental results over uci data sets and the radar high resolution range profile data are presented

2 d exact model matching the exact model matching problem in single input single output 2 d systems is solved by applying polyaomial techniques all solutions are described in a parametric form natural solvability conditions for causal and or stable solutions are given and a simple design procedure is proposed it consists in solving a linear 2 d polynomial equation which directly yields a suitable compensator

the admissibility of the kaplan meier and other maximum likelihood estimators in the presence of censoring for the nonparametric estimation of a survival function when censoring is present the kaplan meier estimator is often used the admissibility of this estimator and other related maximum likelihood estimators is demonstrated this is done by reducing the problem to one involving just the multinomial distribution and then using the stepwise bayes technique to prove admissibility 1 introduction the admissibility of various well known nonparametric estimators was recently demonstrated in meeden ghosh and vardeman 1985 the argument proving admissibility involved two essential steps the first was to note that to prove admissibility for the nonparametric problem it was enough to prove admissibility when the family of possible distributions was just assumed to be some multinomial family of distributions rather than the large nonparametric family the second step then used the stepwise bayes technique to prove admissibility for the multinomial problem here we will use the same argument to prove the admissibility of the kaplan meier estimator for the nonparametric estimation of the survival function when censoring is present in fact the admissibility of a whole class of maximum likelihood estimators which includes the kaplan meier estimator is demonstrated the only additional complication is that the censoring mechanism must be modeled and then taken into account this description and modeling of the censoring is carried out in section 3 in section 4 the stepwise bayes technique is used to demonstrate the admissibility of the maximum likelihood estimator for the multinomial problem in section 5 the admissibility results for the nonparametric problem are given if we were just proving the admissibility of the kaplan meier estimator this paper would be considerably shorter although the proofs for the kaplan meier estimator and the more general case are essentially the same the general case requires considerably more notation for someone who is just interested in the kaplan meier estimator the argument can be simplified using the notation of

a boundary method for outlier detection based on support vector domain description the support vector domain description svdd is a popular kernel method for outlier detection which tries to fit a class of data with a sphere and uses a few target objects to support its decision boundary the problem is that even with a flexible gaussian kernel function the svdd could sometimes generate such a loose decision boundary that the discrimination ability becomes poor therefore a computationally intensive procedure called kernel whitening is often required to improve the performance in this paper we propose a simple post processing method which tries to modify the svdd boundary in order to achieve a tight data description with no need of kernel whitening with the derivation of the distance between an object and its nearest boundary point in input space the proposed method can efficiently construct a new decision boundary based on the svdd boundary the improvement from the proposed method is demonstrated with synthetic and real world datasets the results show that the proposed decision boundary can fit the shape of synthetic data distribution closely and achieves better or comparable classification performance on real world datasets

regulation of split linear systems over rings coefficient assignment and observers a theory of regulators is developed for finite free split linear systems over a commutative ring k this is achieved by developing a theory of coefficient assignment and observers it is shown that the problem of coefficient assignment can be solved for reachable systems by using dynamic state feedback for strongly observable systems it is shown that observers with arbitrary dynamics can be constructed internal stability of observer and state feedback configuration is considered explicitly some examples are given to illustrate these techniques

a bayesian bootstrap for censored data a bayesian bootstrap for a censored data model is introduced its small sample distributional properties are discussed and found to be similar to efron s bootstrap for censored data in the absence of censoring the bayesian bootstrap for censored data reduces to rubin s bayesian bootstrap for complete data a first order large sample theory is developed this theory shows that both censored data bootstraps are consistent bootstraps for approximating the sampling distribution of the kaplan meier estimator it also shows that both bootstraps are consistent bootstraps for approximating a posterior distribution of the survival function with respect to each member of the class of conjugate beta neutral process priors

tests for regulability of 2 d systems regulability tests of 2 d systems are transformed into stability tests of one variable polynomials in this paper the method can be used in regulability tests of multi dimensional systems without any substantial modification

a geometric approach to the theory of 2 d systems the authors develop a geometric approach to the theory of 2 d two dimensional systems defining in a suitable way the notion of a sub 1 2 b sub 1 2 invariant subspaces and of controlled invariant subspaces such subspaces are shown to have good computational and feedback properties which make them useful in application in particular sufficient conditions and constructive procedures are obtained for the solutions of disturbance decoupling and model matching problems moreover it is shown that certain structural properties of a 2 d system can be described by means of a set of indexes defined in geometric terms and that such structural indexes can be used to reformulate the sufficient condition for model matching

pole assignment by dynamic feedback it is shown that by dynamic feedback the closed loop transfer matrix of a linear system can be made equal to a proper rational matrix of the form v s t 1 s t s t k s here v s is the numerator polynomial matrix of the open loop transfer matrix t s is a polynomial matrix which can be chosen arbitrarily up to some degree constraints t 2 s is a polynomial matrix whose invariant factors can be chosen arbitrarily up to some degree inequalities t k s is a polynomial matrix which is the denominator matrix in a matrix fraction description of the feedback matrix

optimizing the data dependent kernel under a unified kernel optimization framework the kernel functions play a central role in kernel methods accordingly over the years the optimization of kernel functions has been a promising research area ideally fisher discriminant criteria can be used as an objective function to optimize the kernel function to augment the margin between different classes unfortunately fisher criteria are optimal only in the case that all the classes are generated from underlying multivariate normal distributions of common covariance matrix but different means and each class is expressed by a single cluster due to the assumptions fisher criteria obviously are not a suitable choice as a kernel optimization rule in some applications such as the multimodally distributed data in order to solve this problem recently many improved discriminant criteria dc have been also developed therefore to apply these discriminant criteria to kernel optimization in this paper based on a data dependent kernel function we propose a unified kernel optimization framework which can use any discriminant criteria formulated in a pairwise manner as the objective functions under the kernel optimization framework to employ different discriminant criteria one has to only change the corresponding affinity matrices without having to resort to any complex derivations in feature space experimental results based on some benchmark data demonstrate the efficiency of our method

on the coefficient assignment of two dimensional systems in this note we prove that in two dimensional 2 d systems the arbitrary coefficient assignment cannot be achieved by using causal output feedbacks the problem however can be transformed into the design of systems over rings in order to find conditions guaranteeing the existence of a solution some related problems are also discussed

fisher regularized support vector machine this paper proposes fisher regularized support vector machine fishersvm fishersvm is a graph based supervised learning method fishersvm has two regularization terms maximum margin and fisher regularizations fishersvm aims to maximize the margin and minimize the within class scatter support vector machine svm and fisher discriminant analysis fda are two commonly used methods in machine learning and pattern recognition a combined method of the linear svm and fda called svm lda linear discriminant analysis has been proposed only for the linear case this paper generalizes this combined method to the nonlinear case from the view of regularization a fisher regularization is defined and incorporated into svm to obtain a fisher regularized support vector machine fishersvm in fishersvm there are two regularization terms the maximum margin regularization and fisher regularization which allow fishersvm to maximize the classification margin and minimize the within class scatter roughly speaking fishersvm can approximatively fulfill the fisher criterion and obtain good statistical separability this paper also discusses the connections of fishersvm to graph based regularization methods and the mathematical programming method to kernel fisher discriminant analysis kfda it shows that fishersvm can be thought of as a graph based supervised learning method or a robust kfda experimental results on artificial and real world data show that fishersvm has a promising generalization performance

rapid and brief communication improving support vector data description using local density degree we propose a new support vector data description svdd incorporating the local density of a training data set by introducing a local density degree for each data point by using a density induced distance measure based on the degree we reformulate a conventional svdd experiments with various real data sets show that the proposed method more accurately describes training data sets than the conventional svdd in all tested cases

confidence bands for least squares support vector machine classifiers a regression approach this paper presents bias corrected 100 email protected simultaneous confidence bands for least squares support vector machine classifiers based on a regression framework the bias which is inherently present in every nonparametric method is estimated using double smoothing in order to obtain simultaneous confidence bands we make use of the volume of tube formula we also provide extensions of this formula in higher dimensions and show that the width of the bands are expanding with increasing dimensionality simulations and data analysis support its usefulness in practical real life classification problems

bayesian methods for censored categorical data abstract bayesian methods are given for finite category sampling when some of the observations suffer missing category distinctions dickey s 1983 generalization of the dirichlet family of prior distributions is found to be closed under such censored sampling the posterior moments and predictive probabilities are proportional to ratios of b c carlson s multiple hypergeometric functions closed form expressions are developed for the case of nested reported sets when bayesian estimates can be computed easily from relative frequencies effective computational methods are also given in the general case an example involving surveys of death penalty attitudes is used throughout to illustrate the theory a simple special case of categorical missing data is a two way contingency table with cross classified count data xij i 1 r j 1 c together with supplementary trials counted only in the margin distinguishing the rows yi i 1 r there could also be further supplementary trials report

linear systems with commensurate time delays stability and stabilization independent of delay notions of exponential stability independent of delay and stabilizability independent of delay are developed for the class of delay differential systems of the retarded type with commensurate time delays various criteria for exponential stability independent of delay with a given order are specified in terms of matrices whose entries are functions of a single real parameter and polynomials in one variable whose coefficients are functions of a single real parameter sufficient conditions and a necessary condition based on local stabilizability are given for stabilizability independent of delay using state feedback with commensurate time delays constructive methods for determining a stabilizing feedback are also presented the last part of the paper deals with a standard type of observer and regulator with the requirement that the closed loop system be stable independent of delay

density induced margin support vector machines this paper proposes a new classifier called density induced margin support vector machines dmsvms dmsvms belong to a family of svm like classifiers thus dmsvms inherit good properties from support vector machines svms e g unique and global solution and sparse representation for the decision function for a given data set dmsvms require to extract relative density degrees for all training data points these density degrees can be taken as relative margins of corresponding training data points moreover we propose a method for estimating relative density degrees by using the k nearest neighbor method we also show the upper bound on the leave out one error of dmsvms for a binary classification problem and prove it promising results are obtained on toy as well as real world data sets

a complete class theorem for statistical problems with finite sample spaces this paper contains a complete class theorem theorem 3 2 which applies to most statistical estimation problems having a finite sample space this theorem also applies to many other statistical problems with finite sample spaces the description of this complete class involves a stepwise algorithm at each step of the process it is necessary to construct the bayes procedures in a suitably modified version of the original problem the complete class is a minimal complete class if the loss function is strictly convex some examples are given to illustrate the application of this complete class theorem among these is a new result concerning the estimation of the parameters of a multinomial distribution under a normalized quadratic loss function see example 4 5

comments on 2 d exact model matching two examples are given to indicate that in 2 d coefficient assignment model matching some cancellations are unavoidable in the author s belief and the split condition not only the zero coprimeness is necessary

an eigenvalue based approach for the stabilization of linear time delay systems of neutral type an eigenvalue based approach for the stabilization of linear neutral functional differential equations is presented which extends the recently developed continuous pole placement method for delay equations of retarded type the approach consists of two steps first the stability of the associated difference equation is determined and a procedure is applied to compute the supremum of the real parts of its characteristic roots which corresponds to computing the radius of the essential spectrum of the solution operator of the neutral equation no restrictions are made on the dimension of the system and the number of delays also the effect of small delay perturbations is explicitly taken into account as a result of this first step the stabilization problem of the neutral equation is reduced to a problem involving only a finite number of characteristic roots as a second step stabilization is achieved by shifting the rightmost or unstable characteristic roots to the left half plane in a quasi continuous way by applying small changes to the controller parameters and meanwhile monitoring other characteristic roots with a large real part a numerical example is presented

density induced support vector data description the purpose of data description is to give a compact description of the target data that represents most of its characteristics in a support vector data description svdd the compact description of target data is given in a hyperspherical model which is determined by a small portion of data called support vectors despite the usefulness of the conventional svdd however it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data to address the issue in svdd methodology we propose a new svdd by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set moreover for a real application we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem experiments with various real data sets show promising results

control of linear systems with fixed noncommensurate point delays the first solution is given to the fundamental open problem of stabilizability and detectability necessary sufficient conditions for internal stabilization by feedback of retarded and a large class of neutral delay differential systems with several fixed noncommensurate point delays using causal compensators observers and state feedback or dynamic output feedback which are also the same type of neutral or retarded delay differential systems with fixed point delays only our results are rank conditions on the system matrices zi f g and zif t h t evaluated at points in the complex plane and are the weakest possible generally applicable sufficient such rank conditions for stabilization of neutral systems in the light of what is known on the stability of such systems these conditions are necessary for most practical purposes the class of systems we consider includes all retarded delay differential systems with noncommensurate fixed point delays in the case of retarded systems these rank conditions are necessary and sufficient conditions for stabilization via compensators which are causal retarded delay differential systems with fixed point delays only these constitute the first full solution of these previously unsolved problems of stabilizability and detectability which together are necessary and sufficient conditions for internal stabilization by feedback for delay differential systems even in the retarded single fixed point delay case an application of our results to a problem of practical importance in control of linear systems with no delays provides a stabilization criterion interesting in itself

rapid and brief communiction possibilistic support vector machines we propose new support vector machines svms that incorporate the geometric distribution of an input data set by associating each data point with a possibilistic membership which measures the relative strength of the self class membership by using a possibilistic distance measure based on the possibilistic membership we reformulate conventional svms in three ways the proposed methods are shown to have better classification performance than conventional svms in various tests

the sylvester resultant with applications to systems over rings the sylvester resultant is studied in this paper for systems over rings the systems over rings may have zero coprime fractions fractor coprime fractions and fraction with common factors the sylvester resultant can be used to test these situations it can also be used to achieve the design of coefficient assignment in the first case to test the regulability in the second case and to find the fractor coprime fractions in the third case

extending the family of bayesian bootstraps and exchangeable urn schemes summary a bayesian bootstrap for a finite population with censored observations is introduced it is shown to reduce to the finite population bayesian bootstrap if there is no censoring and to reduce to the censored data bayesian bootstrap for a large population a class of general urn schemes for simulating exchangeable sequences of variables is introduced which is connected to the bootstrap method

on necessary and sufficient conditions for regulation of linear systems over rings necessary and sufficient conditions are given for regulation of linear systems over rings using observers and causal dynamic state feedback systems with the polynomial fractional representation property the results are then used to obtain stabilizability conditions for systems over integers delay differential systems systems over polynomial rings and to obtain conditions to make a 2 d system nonrecursive

two class support vector data description support vector data description svdd is a data description method that can give the target data set a spherically shaped description and be used to outlier detection or classification in real life the target data set often contains more than one class of objects and each class of objects need to be described and distinguished simultaneously in this case traditional svdd can only give a description for the target data set regardless of the differences between different target classes in the target data set or give a description for each class of objects in the target data set in this paper an improved support vector data description method named two class support vector data description tc svdd is presented the proposed method can give each class of objects in the target data set a hypersphere shaped description simultaneously if the target data set contains two classes of objects the characteristics of the improved support vector data descriptions are discussed the results of the proposed approach on artificial and actual data show that the proposed method works quite well on the 3 class classification problem with one object class being undersampled severely

robust observers for systems with parameters abstract in many cases linear multivariable models which describe physical systems contain structured parametric type uncertainties specifically it may be that the state space model is expressed in a form which involves parameters we first consider circumstances in which a parameter independent observer can be constructed for the system and suggest conditions that guarantee its existence we also give conditions under which such a robust observer can be subsequently used with additional dynamics in a parameter free configuration for regulation

a constructive algorithm for the complete set of compensators for two dimensional feedback system design in the recent factorization approach to linear feedback system design it has been found that in order to completely solve a generalized stabilization problem a coprime condition in the ring of stable transfer functions must be solved in this note two dimensional 2 d systems are considered and this stable coprime condition is solved for stable rings a well known 2 d zero coprime condition is a special case of this condition this is the first constructive method for obtaining the complete set of solutions or compensators for 2 d feedback system design some examples of systems treated as 2 d systems are temporal spatial spatial spatial delay differential multipass etc

estimating the support of a high dimensional distribution suppose you are given some data set drawn from an underlying probability distribution p and you want to estimate a simple subset s of input space such that the probability that a test point drawn from p lies outside of s equals some a priori specified value between 0 and 1 we propose a method to approach this problem by trying to estimate a function f that is positive on s and negative on the complement the functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data it is regularized by controlling the length of the weight vector in an associated feature space the expansion coefficients are found by solving a quadratic programming problem which we do by carrying out sequential optimization over pairs of input patterns we also provide a theoretical analysis of the statistical performance of our algorithm the algorithm is a natural extension of the support vector algorithm to the case of unlabeled data

maxi min margin machine learning large margin classifiers locally and globally in this paper we propose a novel large margin classifier called the maxi min margin machine m4 this model learns the decision boundary both locally and globally in comparison other large margin classifiers construct separating hyperplanes only either locally or globally for example a state of the art large margin classifier the support vector machine svm considers data only locally while another significant model the minimax probability machine mpm focuses on building the decision hyperplane exclusively based on the global information as a major contribution we show that svm yields the same solution as m4 when data satisfy certain conditions and mpm can be regarded as a relaxation model of m4 moreover based on our proposed local and global view of data another popular model the linear discriminant analysis can easily be interpreted and extended as well we describe the m4 model definition provide a geometrical interpretation present theoretical justifications and propose a practical sequential conic programming method to solve the optimization problem we also show how to exploit mercer kernels to extend m4 for nonlinear classifications furthermore we perform a series of evaluations on both synthetic data sets and real world benchmark data sets comparison with svm and mpm demonstrates the advantages of our new model

output feedback stabilization of delay systems via generalization of the transformation method the left characteristic matrix equation l c m e of a delay system has been employed to develop a state feedback stabilization theory for delay systems analogously the right characteristic matrix equation r c m e has led to the development of an observer theory for this class of systems the characteristic matrix equation approach however only permits the relocation of integral multiples of n poles where n represents the system order this restriction is removed in this work through a generalization of the l c m e and r c m e in a manner which permits the stabilization of an arbitrary but finite number of unstable modes the resulting state feedback controller and state estimator permit the output feedback stabilization of the most general linear autonomous delay system under the minimal assumptions of spectral stabilizability and detectability

a locality correlation preserving support vector machine abstract this paper proposes a locality correlation preserving based support vector machine lcpsvm by combining the idea of margin maximization between classes and local correlation preservation of class data it is a support vector machine svm like algorithm which explicitly considers the locality correlation within each class in the margin and the penalty term of the optimization function canonical correlation analysis cca is used to reveal the hidden correlations between two datasets and a variant of correlation analysis model which implements locality preserving has been proposed by integrating local information into the objective function of cca inspired by the idea used in canonical correlation analysis we propose a locality correlation preserving within class scatter matrix to replace the within class scatter matrix in minimum class variance support machine mcvsvm this substitution has the property of keeping the locality correlation of data and inherits the properties of svm and other similar modified class of support vector machines lcpsvm is discussed under linearly separable small sample size and nonlinearly separable conditions and experimental results on benchmark datasets demonstrate its effectiveness

pole assignment of two dimensional systems for separable characteristic equations abstract this paper considers the problem of pole assignment of linear time invariant multivariable two dimensional systems the desirable closed loop characteristic function is considered to be separable an algorithm is presented for the solution of the problem using state feedback the necessary condition for the problem to have a solution is established and an illustrative example is given

the dynamic feedback equivalence over principal ideal domains we show that over a principal ideal domain the dynamic feedback equivalence for not necessarily reachable linear systems is reduced to the feedback equivalence for one augmented systems we also obtain the dynamic feedback classification for two dimensional linear systems

on matrix fraction representations for linear systems over commutative rings this paper deals with the problem of existence of polynomial matrix fraction representations for transfer matrices of linear systems over rings as well as the related realization theory these representations are then used in establishing new results for various classes of systems including split systems the relevance of these results to the regulation of various nonclassical classes of linear systems for example delay differential systems two dimensional systems etc is also discussed

face recognition using total margin based adaptive fuzzy support vector machines this paper presents a new classifier called total margin based adaptive fuzzy support vector machines taf svm that deals with several problems that may occur in support vector machines svms when applied to the face recognition the proposed taf svm not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm in addition by introducing the total margin algorithm to replace the conventional soft margin algorithm a lower generalization error bound can be obtained those three functions are embodied into the traditional svm so that the taf svm is proposed and reformulated in both linear and nonlinear cases by using two databases the chung yuan christian university cycu multiview and the facial recognition technology feret face databases and using the kernel fisher s discriminant analysis kfda algorithm to extract discriminating face features experimental results show that the proposed taf svm is superior to svm in terms of the face recognition accuracy the results also indicate that the proposed taf svm can achieve smaller error variances than svm over a number of tests such that better recognition stability can be obtained

robust controllers for multivariable model matching systems abstract the robustness properties of multivariable perfect model matching systems are presented in order to stabilize the nominal system a general parametrized 2 d controller is introduced a necessary and sufficient condition of robust stabilization is derived for the perfect model matching system under external disturbances and non linear time varying perturbations the results presented could be viewed as the extension of the work by astrom 1980