基于距离度量学习的DCT域JPEG图像检索

由于特征有限,传统基于欧式距离的压缩域检索性能并不理想。本文引入距离度量学习技术,研究压缩域图像检索,提出了一种基于距离度量学习的离散余弦变换（DCT）域联合图像专家小组（JPEG）图像检索方法。首先,提出了一种更有效的 DCT 域特征提取方法;其次,运用距离度量学习技术训练出一个更加有效的度量矩阵进行检索。在 Corel5000上的图像检索实验表明,新方法有效提高了检索准确度。     

Learning Rates of Least-Square Regularized Regression

This paper considers the regularized learning algorithm associated with the least-square loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regression problem in learning theory. A novel regularization approach is presented, which yields satisfactory learning rates. The rates depend on the approximation property and on the capacity of the reproducing kernel Hilbert space measured by covering numbers. When the kernel is C^∞ and the regression function lies in the corresponding reproducing kernel Hilbert space, the rate is m^ζ with ζ arbitrarily close to 1, regardless of the variance of the bounded probability distribution.     

Bootstrapping complex functions

We formulate a new family of bootstrap algorithms suitable for learning non-Boolean functions from data. Within the Algorithmic Inference framework, the key idea is to consider a population of functions that are compatible with the observed sample. We generate items of this population from standard random seeds and reverse seed probabilities on the items. In this way we may compute in principle, and effectively achieve on paradigmatic examples, direct estimates and confidence intervals for any kind of complex function underlying the observed data according to any hypothesis on the randomness affecting the sample.     

The bi-objective fuzzy c-means cluster analysis for TSK fuzzy system identification

For conventional fuzzy clustering-based approaches to fuzzy system identification, a fuzzy function is used for cluster formation and another fuzzy function is used for cluster validation to determine the number and location of the clusters which define IF parts of the rule base. However, the different fuzzy functions used for cluster formation and validation may not indicate the same best number and location of the clusters. This potential disparity motivates us to propose a new fuzzy clustering-based approach to fuzzy system identification based on the bi-objective fuzzy c-means (BOFCM) cluster analysis. In this approach, we use the BOFCM function for both cluster formation and validation to simultaneously determine the number and location of the clusters which we hope can efficiently and effectively define IF parts of the rule base. The proposed approach is validated by applying it to the truck backer-upper problem with an obstacle in the center of the field.     

基于$\alpha$\,-混合序列的学习机器一致收敛速率的界

Vapnik, Cucker和Smale已经证明了, 当样本的数目趋于无限时, 基于独立同分布序列学习机器的经验 风险会一致收敛到它的期望风险\bd 本文把这些基于独立同分布序列的结果推广到了$\alpha$\,-混合序列, 应用Markov不等式得到了基于$\alpha$\,-混合序列的学习机器一致收敛速率的界     

模糊规则提取的两种方法性能分析

机器学习近年来得到越来越多的重视，模糊规则提取是其中的重要的一个方向。本文介绍了两种自动提取模糊规则的方法，分别是基于多层前向网络和基于遗传算法的模糊规则自动生成。并且，详细的分析了两种方法性能     

Approximation Methods for Supervised Learning

Let ρ be an unknown Borel measure defined on the space Z := X × Y with X ⊂ ℝ^d and Y = [-M,M]. Given a set z of m samples z_i =(x_i,y_i) drawn according to ρ, the problem of estimating a regression function f_ρ using these samples is considered. The main focus is to understand what is the rate of approximation, measured either in expectation or probability, that can be obtained under a given prior f_ρ ∈ Θ, i.e., under the assumption that f_ρ is in the set Θ, and what are possible algorithms for obtaining optimal or semioptimal (up to logarithms) results. The optimal rate of decay in terms of m is established for many priors given either in terms of smoothness of f_ρ or its rate of approximation measured in one of several ways. This optimal rate is determined by two types of results. Upper bounds are established using various tools in approximation such as entropy, widths, and linear and nonlinear approximation. Lower bounds are proved using Kullback-Leibler information together with Fano inequalities and a certain type of entropy. A distinction is drawn between algorithms which employ knowledge of the prior in the construction of the estimator and those that do not. Algorithms of the second type which are universally optimal for a certain range of priors are given.     

Embedding sample points uncertainty measures in learning algorithms

Learning algorithms consider a sample consisting of pairs (pattern, label) and output a decision rule, possibly: (i) associating each pattern with the corresponding label, and (ii) generalizing to new patterns drawn from the same distribution of the original sample. This work proposes a set of methodologies to be applied to existing learning strategies in order to deal with more complex kinds of data sets, carrying also a quantitative measure on the quality of each label.     

基于α-混合序列的学习机器一致收敛速率的界

Vapnik,Cucker和Smale已经证明了,当样本的数目趋于无限时,基于独立同分布序列学习机器的经验风险会一致收敛到它的期望风险.本文把这些基于独立同分布序列的结果推广到了α-混合序列,应用Markov不等式得到了基于α-混合序列的学习机器一致收敛速率的界.     

Output learning and duality in joint production programs

Summary Output learning is incorporated into a short-run static cost-minimizing model of the multiproduct, multifactor firm which employs a fixed-coefficients technology. The firm's output processes or activities are ultimately specified as functions of the activity variables themselves, thus rentering a generalization to a concave program. A Lagrange dual formulation is then used to obtain the indirect cost objective. Given that this optimal cost function is differentiable and satisfies a regularity condition, its price derivatives serve as input demand functions while its derivatives with respect to the minimum output requirements yield a set of (implicit) marginal costs or dual variables.