关于学问的分类

现在广泛通用的学问分类法是以文理科的二分法为前提,理科领域统称“自然科学”,文科则分为“人文科学”和“社会科学”。为了明确确定不断高度细分化的各个专业学科在学问世界的整体图象中的位置,我提出以下三分法,即:

Kuhn and Taxonomies of History

This paper introduces the idea that if theories of history generate different taxonomies of history they too are incommensurable. I argue this is unavoidable for Kuhn given what he says about incommensurability and 1 investigate the consequences in relation to reflexivity, justification, and paradox for Kuhn＇s account of science. I want to do this on two levels, firstly looking at different possibilities for characterising individual paradigms. I will look at some examples from ancient and early modem astronomy as here it is clearest that paradigms can be characterised in different ways and that this has important consequences. I will argue in particular that Kuhn＇s characterisation of the paradigm for astronomy which emerges from antiquity （geocentrism） is favourable to his general account of the history of science, but that there is a very plausible and extremely damaging alternative. I argue that these differing characterisations generate differing, incommensurable taxonomies of the history of astronomy, with attendant ＂local holism,＂ untranslatability of key terms and issues of theory choice. If so, Kuhn then has problems with generating an adequate decision making protocol for choosing between the two paradigm characterisations. That is problematic in itself, but I also argue this problem is systemic and affects the evidence needed for Kuhn to justify his general account of the history of science. I also want to investigate the implications of differing taxonomies of the history of science at a more abstract level. Kuhn＇s general theory of the history of science generates a taxonomy of the history of science, as do other theories such as those of Popper and of gradualism. If so, the incommensurability involved here, again with attendant ＂local holism,＂ untranslatability of key terms and issues of theory choice, leads to issues of paradox and justification for Kuhn＇s general account of the history of science. With this broader understanding of taxonomic issues, some important Kuhn statements about scientific theories become self-referential, again generating problems of paradox and justification.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.

Hybridization and the species problem in conservation

Most biologists recognize the ＂species phenomenon＂ as a real pattern in nature： Biodiversity is characterized by dis- continuities between recognizable groups classified as species. Many conservation laws focus on preventing species extinction. However, species are not fixed. Discontinuities evolve gradually and sometimes disappear. Exactly how to define particular spe- cies is not always obvious. Hybridization between taxonomic species reminds us that species classification is not a perfect repre- sentation of nature. Classification is a model that is very useful, but not adequate in all cases. Conservationists often confront questions about how to apply species-based laws when hybridization confounds classification. Development of sophisticated techniques and nuanced interpretation of data in the basic study of species and speciation has exposed the need for deeper educa- tion in genetics and evolution for applied conservationists and decision makers. Here we offer a brief perspective on hybridiza- tion and the species problem in conservation. Our intended audience is conservation practitioners and decision-makers more than geneticists and evolutionary biologists. We wish to emphasize that the goals and premises of legislative classification are not identical to those of scientific classification. Sometimes legal classification is required when the best available science indicates that discrete classification is not an adequate model for the case. Establishing legal status and level of protection for hybrids and hybrid populations means choosing from a range of scientifically valid alternatives. Although we should not abandon species-based approaches to conservation, we must recognize their limitations and work to clarify the roles of science and values in ethical and legal decisions [Current Zoology 61 （1）： 206-216, 2015].

Variable Sets principle and method for flood classification

Flood classification is an effective way to improve flood forecasting accuracy. According to the opposite unity mathematical theorem in Variable Sets theory, this paper proposes a Variable Sets principle and method for flood classification, which is based on the mathematical theorem of dialectics basic laws. This newly proposed method explores a novel way to analyze and solve engineering problems by utilizing a dialectical thinking. In this paper, the Tuwei River basin, located in the Yellow River tributary, is taken as an example for flood classification. The results obtained in this study reveal the problems in a previous method—Set Pair Analysis classification method. The variable sets method is proven to be theoretically rigorous, computationally simple. The classification results are objective, accurate and consistent with the actual situations. This study demonstrates the significant importance of using a scientifically sound method in solving engineering problems.