In solving pattern recognition problems, many ensemble methods have been proposed to replace a single classifier by a classifier committee. These methods can be divided roughly into two categories: serial and parallel approaches. In the serial approach, component classifiers are created by focusing on different parts of the training set in different learning phases. In contrast, without paying ...

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A novel face recognition method based on multi-features using a neural networks committee (NNC) machine is proposed in this paper. The committee consists of several independent neural networks trained by different image blocks of the original images in different feature domains. The final classification results represent a combined response of the individual networks. Then, we use the designed ...

Extreme learning machine (ELM) is an efficient learning algorithm for single-hidden layer feedforward networks (SLFN). This paper proposes the combination of ELM networks using a regularized committee. Simulations on many real-world regression data sets have demonstrated that this algorithm generally outperforms the original ELM algorithm.

Many of today’s scientific advancements in the field of medicine get their start in radiology. “Research and development in radiology will continue to transform and revolutionize medicine through better and faster disease detection, diagnosis and treatment,” said John Renz, MD, RSNA Technical Exhibits Committee chair. “So much of what is changing and improving patient care and healthcare is a r...

Gaussian process regression allows a simple analytical treatment of exact Bayesian inference and has been found to provide good performance, yet scales badly with the number of training data. In this paper we compare experimentally three of the leading approaches towards scaling Gaussian processes regression to large data sets: the subset of representers method, the reduced rank approximation, ...

On-line supervised learning in the general K Tree Committee Machine (TCM) is studied for a uniform distribution of inputs. Examples are corrupted by noise in the teacher output. From the diierential equations which describe the learning dynamics, the algorithm which optimizes the generalization ability is exactly obtained. For a large number of presented examples, the asymptotical behaviour of ...

In on-line machine learning, predicting changes is not a trivial task. In this paper, a novel prediction approach is presented, that relies on a committee of experts. Each expert is trained on a specific history of changes and tries to predict future changes. The experts are constantly modified based on their performance and the committee as a whole is thus dynamic and can adapt to a large vari...

The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity (αc = 16 π √ lnK) of large committee machines, to verify the stability of the solution in the limit of a large number K of hidden units and to find a Bayesian generalization cross–over at α = K. PACS Numbers : 05.20 64.60 87.10 Typeset using...

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang tran...