Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.

Primary Health Care Providers' Perspectives: Facilitating Older Patients' Access to Community Support Services

The purpose of the study examined in this article was to understand how non-physician health care professionals working in Canadian primary health care settings facilitate older persons’ access to community support services (CSSs). The use of CSSs has positive impacts for clients, yet they are underused from lack of awareness. Using a qualitative description approach, we interviewed 20 health care professionals from various disciplines and primary health care models about the processes they use to link older patients to CSSs. Participants collaborated extensively with interprofessional colleagues within and outside their organizations to fi nd relevant CSSs. They actively engaged patients and families in making these linkages and ensured follow-up. It was troubling to fi nd that they relied on out-of-date resources and ineffi cient search strategies to fi nd CSSs. Our fi ndings can be used to develop resources and approaches to better support primary health care providers in linking older adults to relevant CSSs.