Raftul cu initiativa Book Archive

Machine Theory

The mathematical foundations of learning machines by Nils J Nilsson

By Nils J Nilsson

Show description

Read or Download The mathematical foundations of learning machines PDF

Similar machine theory books

Digital and Discrete Geometry: Theory and Algorithms

This e-book offers accomplished assurance of the trendy tools for geometric difficulties within the computing sciences. It additionally covers concurrent subject matters in information sciences together with geometric processing, manifold studying, Google seek, cloud info, and R-tree for instant networks and BigData. the writer investigates electronic geometry and its comparable confident tools in discrete geometry, supplying distinctive tools and algorithms.

Artificial Intelligence and Symbolic Computation: 12th International Conference, AISC 2014, Seville, Spain, December 11-13, 2014. Proceedings

This publication constitutes the refereed complaints of the twelfth overseas convention on synthetic Intelligence and Symbolic Computation, AISC 2014, held in Seville, Spain, in December 2014. The 15 complete papers offered including 2 invited papers have been rigorously reviewed and chosen from 22 submissions.

Statistical Language and Speech Processing: Third International Conference, SLSP 2015, Budapest, Hungary, November 24-26, 2015, Proceedings

This publication constitutes the refereed complaints of the 3rd foreign convention on Statistical Language and Speech Processing, SLSP 2015, held in Budapest, Hungary, in November 2015. The 26 complete papers awarded including invited talks have been conscientiously reviewed and chosen from seventy one submissions.

Extra info for The mathematical foundations of learning machines

Sample text

3, however, the gbs of a version space for terms need not be singular. 3 Compare this view of top-down versus bottom-up with the divide-and-conquer and the covering (or AQ) methods of decision-tree induction. Learning as Search of a Version Space [To be written. Relate to term learning algorithm presented in Chapter Two. Also discuss best-first search methods. ] Selecting a hypothesis from the version space can be thought of as a search problem. One can start with a very general function and specialize it through various specialization operators until one finds a function that is consistent (or adequately so) with a set of training patterns.

The threshold, θ, is set equal to kp − 1/2, where kp is the number of positive literals in the term. Such a TLU implements a hyperplane boundary that is 38 CHAPTER 4. NEURAL NETWORKS parallel to a subface of dimension (n − k) of the unit hypercube. We show a three-dimensional example in Fig. 3. Thus, linearly separable functions are a superset of terms. 3: Implementing a Term Clauses The negation of a clause is a term. For example, the negation of the clause f = x1 + x2 + x3 is the term f = x1 x2 x3 .

7. Starting at V1 , we see that it gives an incorrect response for pattern Y1 , so we move V1 to V2 in a direction normal to plane 1. ) Y2 gives an incorrect response for pattern Y2 , and so on. Ultimately, the responses are only incorrect for planes bounding the solution region. Some of the subsequent corrections may overshoot the solution region, but eventually we work our way out far enough in the solution region that corrections (for a fixed increment size) take us within it. The proofs for convergence of the fixed-increment rule make this intuitive argument precise.

Download PDF sample

Rated 4.74 of 5 – based on 19 votes