This paper introduces t-OLM, an open learner model to investigate user trust in a range of learner model externalisations. It was found that, while sufficient levels of use were made to suggest user trust in an externalised learner model, the relationship between trust and perceived utility, perceived accuracy, and level of understanding of the model representations, is not straightforward.

This paper presents an application of Fuzzy Theory for Adaptive Learning Diagnosis System (FADS), which consists of five parts: calculating the concept similarity of different chapters; producing the personal adaptive reading mark automatically; calculating the learning degree of each chapter; constructing the fuzzy membership function; and evaluating the adaptive items selection mechanism. The...

This paper aims to use a large set of feature descriptions as geometric cues to build the structural knowledge of an indoor image. In this paper, a large quantity of training images are used to obtain the required information through learning. We apply a multi-class version of AdaBoost with weak learners based on the decision tree to label regions in an indoor image as “ground”, “wall” and “cei...

Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels ...

The present paper deals with monotonic and dual monotonic language learning from positive and negative examples. The three notions of monotonicity re BLOCKINect dierent formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt. The three versions of dual monotonicity describe the concept tha...

In this paper, we study a generalization of z-ideals in the ring C(X) of continuous real valued functions on a completely regular Hausdorff space X. The notion of a weak ideal and naturally a weak z-ideal and a prime weak ideal are introduced and it turns out that they behave such as z-ideals in C(X).

In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct...

Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep residual network (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct T weak module classifiers, each contains two of the T layers, such that the combined strong learner is a ResNet. Therefore,...