The paper describes Pade, the crucial method developed within the project. Pade is a new method for qualitative learning which estimates partial derivatives of the target function from training data and uses them to induce qualitative models of the target function. We formulated three methods for computation of derivatives, all based on using linear regression on local neighbourhoods. The methods were empirically tested on artificial and real-world data. We also provide a case study which shows how the developed methods can be used in practice.
COBISS.SI-ID: 8324436
The paper describes methods that preceeded those published in the paper in Artificail Intelligence Journal (AIJ). They are based on topological analysis and fitting hyper-planes to the data instead of direct application of linear regression like in the AIJ paper. The material was initially prepared for AIJ. The preparation of the paper required unusually long time, in which we already developed a set of new methods that were more suitable for publication in AIJ. The older methods were however stil worthy of publication, which is why we chose Informatica, a journal indexed only by INSPEC and not SCI. (The two papers naturally do not overlap.)
COBISS.SI-ID: 8863572