Interactive techniques leverage the expert knowledge of users to produce accurate image segmentations. However, the segmentation accuracy varies with the users. Furthermore, users require some training with the algorithm and its exposed parameters to obtain the best segmentation with minimal effort. Our work combines active learning with interactive segmentation to (i) achieve the same accuracy...

This paper describes a technique for learning both the number of states and the topology of Hidden Markov Models from examples. The induction process starts with the most specific model consistent with the training data and generalizes by successively merging states. Both the choice of states to merge and the stopping criterion are guided by the Bayesian posterior probability. We compare our al...

The error in an artificial neural network is a function of adaptive parameters (weights and biases) that needs to be minimized. Research on adaptive learning usually focuses on gradient algorithms that employ problem–dependent heuristic learning parameters. This fact usually results in a trade–off between the convergence speed and the stability of the learning algorithm. The paper investigates ...

In this paper, a gradient-based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro-fuzzy system. Premise and consequent parameters of the neuro-fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output wi...

multi agent markov decision processes (mmdps), as the generalization of markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for multi agent reinforcement learning. in this paper, a generalized learning automata based algorithm for finding optimal policies in mmdp is proposed. in the proposed algorithm, mmdp ...

Data compression methods can be classified into two groups: lossless and lossy. Usually the latter achieves a higher compression ratio than the former. However, to develop a lossy compression method, we have to know, for a given type of data, what information can be discarded without significant degradation of the data quality. A usual way to obtain such knowledge is by experiments. For example...

  Abstract   Background : Leukemia is one of the mostcommon cancers in children, comprising more than a third of all   childhood cancers. Newly affected patients in USA are estimated as 10100cases, and if these cases are diagnosed late or proper treatment is not applied, then it can be mortal. Because rapid and proper diagnosis of leukemia based on clinical or medicinal findings (without biopsy...

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven metho...

The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden la...

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in principle. The goal of this paper is to investigate the computational feasibilit...