I'm working on Chapter 5 from The elements of statistical learning which describes the more general linear models that is splines. The fragment (chapter 5, page 143) below describes the alternative and more direct method of building continuous splines.
To be honest I don't really know how does that base work. The previous method used indicators and then two constraints for continuity which was quite intuitive. What does ''the positive'' part mean?
Is the final function $f(X) = \sum \beta_i h_i(X)$ just a sum of $h_1, h_2, h_3, h_4$ described above or do I need some indicators too?