A Classification of Feminist Theories

In this paper I criticize Alison Jaggar’s descriptions of feminist political theories. I propose an alternative classification of feminist theories that I think more accurately reflects the multiplication of feminist theories and philosophies. There are two main categories, “street theory” and academic theories, each with two sub-divisions, political spectrum and “differences” under street theory, and directly and indirectly political analyses under academic theories. My view explains why there are no radical feminists outside of North America and why there are so few socialist feminists inside North America. I argue, controversially, that radical feminism is a radical version of liberalism. I argue that “difference” feminist theories – theory by and about feminists of colour, queer feminists, feminists with disabilities and so on – belong in a separate sub-category of street theory, because they’ve had profound effects on feminist activism not tracked by traditional left-to-right classifications. Finally, I argue that, while academic feminist theories such as feminist existentialism or feminist sociological theory are generally unconnected to movement activism, they provide important feminist insights that may become importanby showing the advantages of my classification over Jaggar’s views.

Identification and classification of geometrical parameeters related to foot pathologies

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.