Learning Pattern ClassificationA Survey

The article applies statistical learning theory to the supervised learning problem. Pattern recognition is covered, including Vapnik-Chervonenkis (VC) theory and the implications for support vector machines (SVMs), neural networks and decision trees. Real predictions are given for scale-sensitive dimensions. The article concludes by analysing large margin classification.

We discuss the problem of model complexity control also known as model selection. This problem frequently arises in the context of predictive learning and adaptive estimation of dependencies from finite data. First we review the problem of predictive learning as it relates to model complexity control. Then we discuss several issues important for practical implementation of complexity control, using the framework provided by Statistical Learning Theory (or Vapnik-Chervonenkis theory). Finally, we show practical applications of Vapnik-Chervonenkis (VC) generalization bounds for model complexity control. Empirical comparisons of different methods for complexity control suggest practical advantages of using VC-based model selection in settings where VC generalization bounds can be rigorously applied. We also argue that VC-theory provides methodological framework for complexity control even when its technical results can not be directly applied.

Model complexity of Genetic Programming (GP) as a learning machine is currently attracting considerable interest from the research community. Here we provide an up-to-date overview of the research concerning complexity measure techniques in GP learning. The scope of this review includes methods based on information theory techniques, such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC); plus those based on statistical machine learning theory on generalization error bound, namely, Vapnik-Chervonenkis (VC) theory; and some based on structural complexity. The research contributions from each of these are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review provides valuable insights into the current GP literature and is a good source for anyone who is interested in the research on model complexity and applying statistical learning theory to GP.

Nelson Goodman’s new riddle of induction forcefully illustrates a challenge that must be confronted by any adequate theory of inductive inference: provide some basis for choosing among alternative hypotheses that fit past data but make divergent predictions. One response to this challenge is to distinguish among alternatives by means of some epistemically significant characteristic beyond fit with the data. Statistical learning theory takes this approach by showing how a concept similar to Popper’s notion of degrees of testability is linked to minimizing expected predictive error. In contrast, formal learning theory appeals to Ockham’s razor, which it justifies by reference to the goal of enhancing efficient convergence to the truth. In this essay, I show that, despite their differences, statistical and formal learning theory yield precisely the same result for a class of inductive problems that I call strongly VC ordered, of which Goodman’s riddle is just one example.

Chapter 7 A survey of computational geometry

The best algorithm for a computational problem generally depends on the “relevant inputs,” a concept that depends on the application domain and often defies formal articulation. While there is a large body of literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. This paper adapts concepts from statistical and online learning theory to reason about application-specific algorithm selection. Our models capture several state-of-the-art empirical and theoretical approaches to the problem, ranging from self-improving algorithms to empirical performance models, and our results identify conditions under which these approaches are guaranteed to perform well. We present one framework that models algorithm selection as a statistical learning problem, and our work here shows that dimension notions from statistical learning theory, historically used to measure the complexity of classes of binary- and re...

Indexability, concentration, and VC theory

Using information technology such as search engine for collecting and monitoring of network public opinion is a practical and effective method. This paper puts forward an improved algorithm of SVM classifier based on incremental learning online, and then implements a topic crawler system for network public opinion and to grab the public opinion.

At present, pattern recognition technology has been widely used in the fields of objects, faces, fingerprints, military target recognition, etc. However, pattern recognition method still has obvious shortcomings when applied to the above fields. It is currently restricted to the use of image information for identification. When the image features of the research object are highly similar, the accuracy of pattern recognition is low and cannot meet the actual application requirements. For example, in the case of mixed true and false targets, it is difficult to obtain satisfactory recognition results using only image information. Aiming at the above problems, a pattern recognition method integrating image information and spectral information is proposed in this paper. Firstly, the image recognition model based on the convolutional neural network model is built to identify object categories based on the semantic features of objects and obtain preliminary recognition results. Then, on the basis of the preliminary recognition results, the measured spectral data of the object (spectrum range 400-1 000 nm, spectral resolution 2 nm) is used to perform true and fake identification of the object based on the back propagation (BP) neural network model. The principle of true and false recognition is that the true and false targets are different in material, causing significant difference in their hyperspectral information. Finally, recognition results are obtained. In order to verify the accuracy of the proposed method, true and fake apples and grapes are used as experimental subjects and the result is that:The recognition accuracy obtained by using only image information is 38.50%, and the recognition accuracy obtained by using only spectral information is 63.00%, however, the recognition accuracy obtained by the method proposed in this paper is 95.00%. Compared with the existing pattern recognition method without spectral information participation, the pattern recognition method using image information and spectral information proposed in this paper improves the pattern recognition accuracy under the mixed condition of true and false targets, and can be widely applied to object recognition, face recognition, fingerprint recognition, military target recognition and other fields.

Background/Objectives: Insurance data analysis can be considered as a way of losses reduction by using data mining. It uses the machine learning, pattern recognition and data base theory for discovering the unknown knowledge. Methods/Statistical Analysis: In this paper, information of 2011, third party insurance of Iran insurance company auto has analyzed in Kohgiluyeh and Boyer Ahmad by using the data mining method. Findings: The results show that using clustering algorithms with acceptable clusters will be able to provide a model to identify affecting factors and to determine the effect of them in the profit and loss of auto third party insurance. Applications/Improvements: The algorithm of K-Means has formed the best clustering with 9 clusters that have relatively good quality. It means that has been able to maximize the distance between the cluster and minimize the within cluster distance.

It has been more than 30 years that statistical learning theory (SLT) has been introduced in the field of machine learning. Its objective is to provide a framework for studying the problem of inference that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data. Support Vector Machine, a method based on SLT, then emerged and becoming a widely accepted method for solving real-world problems. This paper overviews the pattern recognition techniques and describes the state of art in SVM in the field of pattern recognition.

The determination of transcriptional regulatory networks is key to the understanding of biological systems. However, the experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while current computational methods to infer these networks (typically from gene-expression data) achieve only modest accuracy. The latter can be attributed in part to the limitations of a single-organism approach. Computational biology has long used comparative and, more generally, evolutionary approaches to extend the reach and accuracy of its analyses. We therefore use an evolutionary approach to the inference of regulatory networks, which enables us to study evolutionary models for these networks as well as to improve the accuracy of inferred networks. Since the regulatory networks evolve along with the genomes, we consider that the regulatory networks for a family of organisms are related to each other through the same phylogenetic tree. These relationships contain information that can be used to improve the accuracy of inferred networks. Advances in the study of evolution of regulatory networks provide evidence to establish evolutionary models for regulatory networks, which is an important component of our evolutionary approach. We use two network evolutionary models, a basic model that considers only the gains and losses of regulatory connections during evolution, and an extended model that also takes into account the duplications and losses of genes. With the network evolutionary models, we design refinement algorithms to make use of the phylogenetic relationships to refine noisy regulatory networks for a family of organisms. These refinement algorithms include: RefineFast and RefineML, which are two-step iterative algorithms, and ProPhyC and ProPhyCC, which are based on a probabilistic phylogenetic model. For each algorithm we first design it with the basic network evolutionary model and then generalize it to the extended evolutionary model. All these algorithms are computationally efficient and are supported by extensive experimental results showing that they yield substantial improvement in the quality of the input noisy networks. In particular, ProPhyC and ProPhyCC further improve the performance of RefineFast and RefineML. Besides the four refinement algorithms mentioned above, we also design an algorithm based on transfer learning theory called tree transfer learning (TTL). TTL differs from the previous four refinement algorithms in the sense that it takes the gene-expression data for the family of organisms as input, instead of their inferred noisy networks. TTL then learns the network structures for all the organisms at once, meanwhile taking advantage of the phylogenetic relationships. Although this approach outperforms an inference algorithm used alone, it does not perform better than ProPhyC, which indicates that the ProPhyC framework makes good use of the phylogenetic information.

How can we select the best performing data‐driven model? How can we rigorously estimate its generalization error? Statistical learning theory (SLT) answers these questions by deriving nonasymptotic bounds on the generalization error of a model or, in other words, by delivering upper bounding of the true error of the learned model based just on quantities computed on the available data. However, for a long time, SLT has been considered only as an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this review is to give an intelligible overview of the problems of model selection (MS) and error estimation (EE), by focusing on the ideas behind the different SLT‐based approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. We start by presenting the seminal works of the 80s until the most recent results, then discuss open problems and finally outline future directions of this field of research.This article is categorized under:Algorithmic Development > Statistics

<title>Abstract</title> Brain morphology emerges from the interplay of genetic programming and mechanical forces. However, its fractal-like folding patterns, regional anisotropy, and uneven curvature distributions pose longstanding challenges for quantitative analysis in medical and developmental studies. These difficulties are exacerbated by the limited availability of labeled data for existing statistical learning approaches. Here, we introduce a theory-grounded physics-transfer (PT) learning framework that enables generalization analysis and prediction in complex physical systems by leveraging the continuity of underlying physical laws across different levels of complexity. Specifically, mechanistic insights into nonlinear elasticity of simple, analytically tractable geometries are embedded directly into neural networks and then transferred to brain models through a sequence of physics-anchored domains, providing, for the first time, a formulation that integrates explicit physics constraints into learning theory. In this sense, PT advances classical statistical learning and domain adaptation theories by enabling generalization analysis in regimes where purely statistical assumptions are insufficient. Building on PT, we derive a generalization bound that explains the model’s strong performance in feature characterization and morphogenesis prediction for brain. The results reveal that localized deformation modes, rather than global geometry, dominantly shape cortical folding dynamics. Beyond predictive accuracy, the framework also yields reduced-dimensional evolutionary representations that distill the essential physics of the highly folded cerebral cortex. Validation against medical imaging data and expert clinical assessment demonstrates the promise of physics-aware digital-twin technologies for understanding, diagnosing, and ultimately intervening in the morphological complexity of the developing and diseased brain.

This paper reviews recent progress in rotation invariant pattern recognition; the emphasis is on the work done in our own laboratories, since much of the significant work done elsewhere is described in other papers presented at this conference.