Selecting useful groups of features in a connectionist framework.

Suppose for a given classification or function approximation (FA) problem data are collected using l sensors. From the output of the ith sensor, ni features are extracted, thereby generating p = sigma li = 1 ni features, so for the task we have X subset Rp as input data along with their corresponding outputs or class labels Y subset Rc. Here, we propose two connectionist schemes that can simultaneously select the useful sensors and learn the relation between X and Y. One scheme is based on the radial basis function (RBF) network and the other uses the multilayered perceptron (MLP) network. Both schemes are shown to possess the universal approximation property. Simulations show that the methods can detect the bad/derogatory groups of features online and can eliminate the effect of these bad features while doing the FA or classification task.     

Finding r-dominating sets and p-centers of trees in parallel

Let T=(V, E) be an edge-weighted tree with |V|=n vertices embedded in the Euclidean plane. Let IE denote the set of all points on the edges of T. Let X and Y be two subsets of IE and let r be a positive real number. A subset D/spl sube/X is an X/Y/r-dominating set if every point in Y is within distance r of a point in D. The X/Y/r-dominating set problem is to find an X/Y/r-dominating set D* with minimum cardinality. Let p/spl ges/1 be an integer. The X/Y/p-center problem is to find a subset C*/spl sube/X of p points such that the maximum distance of any point in Y from C* is minimized. Let X and Y be either V or IE. In this paper, efficient parallel algorithms on the EREW PRAM are first presented for the X/Y/r-dominating set problem. The presented algorithms require O(log/sup 2/n) time for all cases of X and Y. Parallel algorithms on the EREW PRAM are then developed for the X/Y/p-center problem. The presented algorithms require O(log/sup 3/n) time for all cases of X and Y. Previously, sequential algorithms for these two problems had been extensively studied in the literature. However, parallel solutions with polylogarithmic time existed only for their special cases. The algorithms presented in this paper are obtained by using an interesting approach which we call the dependency-tree approach. Our results are examples of parallelizing sequential dynamic-programming algorithms by using the approach.     

The Noisy Substring Matching Problem

Let T(U) be the set of words in the dictionary H which contains U as a substring. The problem considered here is the estimation of the set T(U) when U is not known, but Y, a noisy version of U is available. The suggested set estimate S*(Y) of T(U) is a proper subset of H such that its every element contains at least one substring which resembles Y most according to the Levenshtein metric. The proposed algorithm for-the computation of S*(Y) requires cubic time. The algorithm uses the recursively computable dissimilarity measure Dk(X, Y), termed as the kth distance between two strings X and Y which is a dissimilarity measure between Y and a certain subset of the set of contiguous substrings of X. Another estimate of T(U), namely SM(Y) is also suggested. The accuracy of SM(Y) is only slightly less than that of S*(Y), but the computation time of SM(Y) is substantially less than that of S*(Y). Experimental results involving 1900 noisy substrings and dictionaries which are subsets of 1023 most common English words [11] indicate that the accuracy of the estimate S*(Y) is around 99 percent and that of SM(Y) is about 98 percent.     

FFT regression and cross-noise reduction for comparing images in remote sensing

In many remote sensing studies it is desired to quantify the functional relationship between images of a given target that were acquired by different sensors. Such comparisons are problematic because when the pixel values of one image are plotted versus the other, the 'cross-noise' is quite high. Typically, the correlation coefficient is quite low, even when the compared images look alike. Nevertheless, we can try to quantify the functional relationship between two images by a suitable regression model function Y = f ( X ), while choosing one of them as 'the reference' Y and using the other one as a 'predictor' X . The underlying assumption of classical regression is that Y is absolutely correct while X is erroneous. Thus, the objective is to fit X to Y by choosing the parameters of Y = f ( X ), which minimize the 'residuals' ( “ - Y ). When comparing images in remote sensing this objective is not valid because Y itself is error prone. The alternative FFT regression method presented herein comprises a two-stage sensor fusion approach, whereby the initially low correlation between X and Y is increased and the residuals are dramatically decreased. First, pairwise image transforms are applied to X and Y whereby the correlation coefficient is increased, e.g. from roughly 0.4 to about 0.8-0.85. A predicted image Y fft is then derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. In the second stage, there are two options: For one time predictions, the phase matrix of Y is combined with the amplitude matrix of Y fft, whereby an improved predicted image Y plock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Y fft versus Y . For long term predictions, the phase matrix of a 'field mask' is combined with the amplitude matrices of the reference image Y and the predicted image Y fft . The field mask is a binary image of a pre-selected region of interest in X and Y . The resultant images Y pref and Y pred are modified versions of Y and Y fft respectively. The residuals of Y pred versus Y pref are even lower than the residuals of Y plock versus Y . Images Y pref and Y pred represent a close consensus of two independent imaging methods which view the same target. The practical utility of FFT regression is demonstrated by examples wherein remotely sensed NDVI images X are used for predicting yield distributions in agricultural fields. Reference yield maps Y were derived by yield monitors which measure the flow rate of the crop while it is being harvested. The 2D FFT transforms, as well as other mathematical operations in this paper were performed in the 'MATLAB' environment.     

Enhancing multi-label classification by modeling dependencies among labels

In this paper, we propose a novel framework for multi-label classification, which directly models the dependencies among labels using a Bayesian network. Each node of the Bayesian network represents a label, and the links and conditional probabilities capture the probabilistic dependencies among multiple labels. We employ our Bayesian network structure learning method, which guarantees to find the global optimum structure, independent of the initial structure. After structure learning, maximum likelihood estimation is used to learn the conditional probabilities among nodes. Any current multi-label classifier can be employed to obtain the measurements of labels. Then, using the learned Bayesian network, the true labels are inferred by combining the relationship among labels with the labels? estimates obtained from a current multi-labeling method. We further extend the proposed multi-label classification method to deal with incomplete label assignments. Structural Expectation-Maximization algorithm is adopted for both structure and parameter learning. Experimental results on two benchmark multi-label databases show that our approach can effectively capture the co-occurrent and the mutual exclusive relation among labels. The relation modeled by our approach is more flexible than the pairwise or fixed subset labels captured by current multi-label learning methods. Thus, our approach improves the performance over current multi-label classifiers. Furthermore, our approach demonstrates its robustness to incomplete multi-label classification.     

基于决策树的回归测试子集选取

软件版本的频繁变更及测试资源的限制要求软件回归测试采用新的测试用例集合的生成和约简技术。为此,介绍基于决策树的回归测试子集的选取方法,将测试用例和测试需求作为一种知识表示系统,对测试知识表示系统进行约简,将约简后的系统构造成一棵决策树,由决策树获得被约简的回归测试子集。理论分析证明该方法复杂度较低。     

An optimal global nearest neighbor metric

A quadratic metric dAO (X, Y) =[(X - Y)T AO(X - Y)]? is proposed which minimizes the mean-squared error between the nearest neighbor asymptotic risk and the finite sample risk. Under linearity assumptions, a heuristic argument is given which indicates that this metric produces lower mean-squared error than the Euclidean metric. A nonparametric estimate of Ao is developed. If samples appear to come from a Gaussian mixture, an alternative, parametrically directed distance measure is suggested for nearness decisions within a limited region of space. Examples of some two-class Gaussian mixture distributions are included.     

Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

This paper presents an empirical study of joint wavelet statistics for textures and other imagery to find an efficient correlation neighborhood. Since there is an established realization that modeling wavelet and other x-let coefficient relationships is crucial to any successful transform domain algorithm (such as Hidden Markov Trees), new works have been devoted to examine these dependencies from different aspects and propose an appropriate model. Because the time and computation complexity involved both in analyzing non-linear dependencies and in solving dependent models may restrict us to consider only a very small subset of contributing neighbors we focus our attention on linear dependencies (correlations) while having a squint on non-linear relations too. In this process, we study a collection of 5000 real images to corroborate our statistical analysis of the joint coefficient behavior and try to find an efficient and at the same time frugal relation map through different statistical means. The statistical observations are then certified by a coefficient significance measure and the competitiveness of the map is substantiated by plugging it into two dependent denoising frameworks.     

柔性协同事务模型

提出了一个ＣＳＣＷ领域中的高级事务处理模型－柔性协同事务模型ＦＣＴＭ。首先给出协同事务的定义。并描述协同事务的状态,然后从协同事务的状态角度刻画协同事务之间、协同事务与外部环境之间的复杂依赖关系,最后用协同事务的状态依赖描述可串行化正确性准则。ＦＣＴＭ的优点在于用户能够根据不同的ＣＳＣＷ应用领域的需求,灵活地定义协同事务的状态和状态依赖。     

Representation of functional dependencies in relational databases using linear graphs

Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) dependency relation, (2) equipotence relation, (3) dissidence relation, (4) completion relation, and dual relations of each of them are described. Any one of these eight relations can be used to represent the functional dependencies in a database. Results from linear graph theory are found helpful in obtaining these representations. The dependency relation directly gives the functional dependencies. The equipotence relation specifies the dependencies in terms of attribute sets which functionally determine each other. The dissidence relation specifies the dependencies in terms of saturated sets in a very indirect way. Completion relation represents the functional dependencies as a function, the range of which turns out to be a lattice. Depletion relation which is the dual of the completion relation can also represent functional dependencies and similarly can the duals of dependency, equipotence, and dissidence relations. The class of depleted sets, which is the dual of saturated sets, is defined and used in the study of depletion relations.     

A new definition of fuzzy functional dependency in fuzzy relational databases

The need to incorporate and treat information given in fuzzy terms in Relational Databases has concentrated a great effort in the last years. This article focuses on the treatment of functional dependencies (f.d.) between attributes of a relation scheme. We review other approaches to this problem and present some of its missfunctions concerning intuitive properties a fuzzy extension of f.d. should verify. Then we introduce a fuzzy extension of this concept to overcome the previous anomalous behaviors and study its properties. of primary interest is the completeness of our fuzzy version of Armstrong axioms in order to derive all the fuzzy functional dependencies logically implied by a set of f.f.d. just using these axioms. © 1994 John Wiley & Sons, Inc.     

Functional and embedded dependency inference: a data mining point of view

The issue of discovering functional dependencies from populated databases has received a great deal of attention because it is a key concern in database analysis. Such a capability is strongly required in database administration and design while being of great interest in other application fields such as query folding. Investigated for long years, the issue has been recently addressed in a novel and more efficient way by applying principles of data mining algorithms. The two algorithms fitting in such a trend are TANE and Dep-Miner. They strongly improve previous proposals. In this paper, we propose a new approach adopting a data mining point of view. We define a novel characterization of minimal functional dependencies. This formal framework is sound and simpler than related work. We introduce the new concept of free set for capturing source of functional dependencies. By using the concepts of closure and quasi-closure of attribute sets, targets of such dependencies are characterized. Our approach is enforced through the algorithm FUN which is particularly efficient since it is comparable or improves the two best operational solutions (according to our knowledge): TANE and Dep-Miner. It makes use of various optimization techniques and it can work on very large databases. Applying on real life or synthetic data more or less correlated, comparative experiments are performed in order to assess performance of FUN against TANE and Dep-Miner. Moreover, our approach also exhibits (without significant additional execution time) embedded functional dependencies, i.e. dependencies captured in any subset of the attribute set originally considered. Embedded dependencies capture a knowledge specially relevant in all fields where materialized data sets are managed (e.g. materialized views widely used in data warehouses).     

Approximate maximum entropy joint feature inference consistent with arbitrary lower-order probability constraints: application to statistical classification

We propose a new learning method for discrete space statistical classifiers. Similar to Chow and Liu (1968) and Cheeseman (1983), we cast classification/inference within the more general framework of estimating the joint probability mass function (p.m.f.) for the (feature vector, class label) pair. Cheeseman's proposal to build the maximum entropy (ME) joint p.m.f. consistent with general lower-order probability constraints is in principle powerful, allowing general dependencies between features. However, enormous learning complexity has severely limited the use of this approach. Alternative models such as Bayesian networks (BNs) require explicit determination of conditional independencies. These may be difficult to assess given limited data. Here we propose an approximate ME method, which, like previous methods, incorporates general constraints while retaining quite tractable learning. The new method restricts joint p.m.f. support during learning to a small subset of the full feature space. Classification gains are realized over dependence trees, tree-augmented naive Bayes networks, BNs trained by the Kutato algorithm, and multilayer perceptrons. Extensions to more general inference problems are indicated. We also propose a novel exact inference method when there are several missing features.     

On interpreting and inferring propositional formulas of data dependencies in a relational database

This paper concerns generally the satisfaction and the inference problem involving functional and/or multivalued dependencies in a relational database. In particular, two independent aids in solving an inference problem, concerning the logical counterparts of functional as well as multivalued dependencies, are introduced. The first aid is provided by establishing a pair of complementary inequivalence and equivalence theorems between the propositional formula corresponding to the difference, U-X, in set theory and the propositional formula not(X) where U is a relation scheme and X is a subset of U. By applying these theorems, correctness of solving an inference problem is assured. The second aid is the application of a Venn diagram for simplifying a propositional formula involving conjunctions, differences, etc., for solving an inference problem. A guideline for constructing simplified Venn diagrams is also given and discussed.     

MEMBERSHIP IN A COMPOUND FUZZY SUBSET

Abstract

We first provide a new formulation for a fuzzy subset as a joint relation of the variables, the element and the membership grade of the element. This relationship is developed by appreciating the fact that a fuzzy subset, as is any set, is a union of the elements which constitute it. With a compound fuzzy subset of X defined as a fuzzy subset defined over a base set whose elements are fuzzy subsets of X we suggest a method for obtaining the membership grade of the elements of X in this compound fuzzy subset. This method uses the formulation suggested at the beginning.     

Multi-relation dependencies

We introduce the class of “multi-relation dependencies” to capture the constraints among relations in a database. While the single relation dependencies are mainly used for database decomposition, the multi-relation dependencies may prove useful in studying the consistency issues regarding modification of data (i.e. insertions, deletions, and updates) in a decomposed database. We will discuss the inference problem of the multi-relation dependencies, and represent a complete set of inference rules for them.     

Database sampling with functional dependencies

During the development of information systems, there is a need to prototype the database that the applications will use when in operation. A prototype database can be built by sampling data from an existing database. Including relevant semantic information when extracting a sample from a database is considered invaluable to support the development of data-intensive applications. Functional dependencies are an example of semantic information that could be considered when sampling a database. This paper investigates how a database relation can be sampled so that the resulting sample satisfies precisely a given set of functional dependencies (and its logical consequences), i.e. is an Armstrong relation.     

Relations between coefficients of the closed-loop characteristic polynomial and output-feedback gains

This paper deals with explicit analytical dependencies between all coefficients of the closed-loop characteristic polynomial and all entries of the output feedback matrix. First, a special kind of non-linear relation that occurs is explained. Next, computationally-efficient formulae are discussed that yield closed-loop characteristic polynomial coefficients with explicit feedback gain dependence. Then it is shown how the closed-loop characteristic polynomial is associated with the transfer function matrix of the open-loop system. Finally, the results obtained are interpreted graph-theoretically.     

A general treatment of dynamic integrity constraints

This paper introduces a general, set-theoretic model for expressing dynamic integrity constraints, i.e., integrity constraints on the state changes that are allowed in a given state space. In a managerial context, such dynamic integrity constraints can be seen as representations of “real world” constraints and business rules. This topic has important practical applications in many business areas. The notions of (direct) transition, reversible and irreversible transition, transition relation, and consistency of a transition relation will be introduced. The expected link with Kripke models (for modal and temporal logics) is also made explicit. Several practical examples of dynamic integrity constraints will illustrate the applicability of the theory. Some important subclasses of dynamic integrity constraints in a database context will be identified, e.g., various forms of cumulativity (which can be regarded as “transitional” inclusion dependencies concerning two different “points in time”), non-decreasing values, integrity constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several formal properties of these dependencies will be derived. For instance, it turns out that functional dependencies can be considered as “degenerated” transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.     

基于树状条件随机场模型的语义角色标注

针对线性条件随机场模型不能清楚表达语义角色内部结构关系的问题,提出一种基于树状条件随机场模型的语义角色标注方法。对句法依存树上的层次依赖关系和兄弟依赖关系进行标注,处理状态变量之间的长距离依赖,利用CRFs模型能添加任意特征的优点,在系统中添加新的组合特征和介词短语角色。在CoNNL 2008 Shared Task语料库上进行实验,结果证明该方法能有效提高系统的准确率和召回率。