Most algorithms to learn belief networks use single-link lookahead search to be efficient. It has been shown that such search procedures are problematic when applied to learning pseudo-independent (PI) models. Furthermore, some researchers have questioned whether PI models exist in practice. We present two non-trivial PI models which derive from a social study dataset. For one of them, the lear...

a reproducing kernel hilbert space restricts the space of functions to smooth functions and has structure for function approximation and some aspects in learning theory. in this paper, the solution of an integral equation of the third kind is constructed analytically using a new method. the analytical solution is represented in the form of series in the reproducing kernel space. some numerical ...

Peter Dayan Brain and Cognitive Sciences E25-210, MIT Cambridge, MA 02139 [email protected] We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using lookup table representations. We illustrate classes of learning curve...

The desire to be good at one's work grows out of the aspiration, competition, and a yearning to be the best. Surgeons, in their aim to provide the best care possible to their patients, adopt this behavior to achieve high levels of expert performance through mastery learning, and the surgical training attempts to prepare them optimally to lead a virtuous and productive life. The proponents of th...

Background: It is important to learn how to study for different examinations. The objective of the current study is to explore whether the assessment method selection would significantly affect the studying and learning approaches of students. Methods: This descriptive-analytical research consisted of 191 first-year undergraduate nursing students from three nursing schools and was conducted dur...

The concept of generalization is deened for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means { in close analogy with supervised learning. The empirical and analytical estimates are compared for Principal Compone...

Performance modelling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system’s internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase. In this paper we investigate a technique, wh...

Analogical modeling (AM) is a memory based model. Known algorithms implementing AM depend on investigating all combinations of matching features, which in the worst case is exponential (O(2)). We formulate a representation theorem on analogical modeling which is used for implementing a range of approximations to AM with a much lower complexity. We will demonstrate how our model can be modified ...

We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal diierence value estimation algorithms change with ooine updates over trials in absorbing Markov chains using lookup table representations. We illustrate classes of learning curve behavior in various chains, and show the manner in which TD is sensitive to the choice of its step-...