Computational capabilities of multilayer committee machines

We obtained an analytical expression for the computational complexity of many layered committee machines with a finite number of hidden layers (L < 8) using the generalization complexity measure introduced by Franco et al (2006) IEEE Trans. Neural Netw. 17 578. Although our result is valid in the large-size limit and for an overlap synaptic matrix that is ultrametric, it provides a useful tool for inferring the appropriate architecture a network must have to reproduce an arbitrary realizable Boolean function.

Learning in ultrametric committee machines

The problem of learning by examples in ultrametric committee machines (UCMs) is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error in UCMs with L hidden layers and for a large enough number of units. In most of the regimes studied we find that the generalization error, as a function of the number of examples presented, develops a discontinuous drop at a critical value of the load parameter. We also find that when L>1 a number of teacher networks with the same number of hidden layers and different overlaps induce learning processes with the same critical points.

Storage capacity of ultrametric committee machines

The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K. © 2014 IOP Publishing Ltd.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Classificação de padrões através de um comitê de máquinas aprimorado por aprendizagem por reforço

Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.

Regional Advisory Committee on Cancer - Second Report 2002 (PDF 132 KB)

Regional Advisory Committee on Cancer (RACC) was established in 1997 to carry forward the recommendations of the Campbell Report of 1996 and to provide advice to the Department of Health and Social Services on the future development of cancer services. The Committee meets twice a year and its membership (Appendix I) is an indication of the wide range of interests involved in Cancer Care across the community. This report records some of the key developments in cancer services over the last 3 years. åÊ Significant progress has been made toward developing a high quality and integrated cancer care network. All five Cancer Units are now operational with chemotherapy and outpatient services for the most common forms of cancer are delivered from these locations. Agreement to the start of the new Cancer Centre, at the Belfast City Hospital, currently estimated to cost å£58m, is expected shortly. As a temporary expedient two additional therapy machines will be installed in Belvoir Park Hospital to increase capacity while the building of the new Cancer Centre proceeds. åÊ To deliver high quality cancer care the workforce needs to continue to expand. This requires increasing investment in the training of professional staff in the context of an already difficult HPSS labour market. The development of the five Cancer Units has increased staff mobility in the short-term, drawing skilled staff away from the centre who have been difficult to replace. At the same time increasing numbers of patients are being offered effective therapies at both the Cancer Units and the Centre. åÊ This report contains a review of selected developments in cancer care. The first section introduces the Memorandum of Understanding and the Tripartite Agreement between the National Cancer Institute of the USA and the Health Departments both North and South. This is a unique international partnership, which promises to bring very significant advantages to both the service and research communities across the Island. åÊ åÊ åÊ

A review of the evidence relating to the copyright law as it applies to jukeboxes in connection with S. 590, a bill relating to the rendition of musical compositions on coin-operated machines.

Mode of access: Internet.

Providing royalties for copyrighted music played on coin-operated machines : report (to accompany H.R. 7194)

"September 10, 1963."

A High magnetic field bubble chamber : report of ORINS-ORNL Committee.

"ORINS-40; Particle accelerators and high-voltage machines."

Paper-cutting machines; a primer of information about paper and card trimmers, hand-lever cutters, power cutters and other automatic machines for cutting paper,

Mode of access: Internet.

S. 368 and election reform : hearing before the Committee on Commerce, Science, and Transportation, United States Senate, One Hundred Seventh Congress, first session, May 8, 2001.

Distributed to some depository libraries in microfiche.

Site Machines

Thesis (Ph.D.)--University of Washington, 2016-07

Vancomycin use in a brazilian teaching hospital: comparison with the Hospital Infection Controlpractices Advisory Committee Guidelines (HICPAC)

This study describes vancomycin prescribing patterns in an average complexity hospital and compare the guidelines proposed by the Hospital Infection Control Practices Advisory Committee (HICPAC). The study was conducted in a 256-bed secondary-care hospital. Data were collected of all patients given vancomycin from March 2003 to February 2004, using a standardized chart-extraction form designed. Appropriate and inappropriate use was reviewed according to the Hospital Infection Control Practices Advisory Committee (HICPAC) guidelines on prudent vancomycin use. Out of 118 prescriptions, 95 (80.5%) were considered appropriate. Out of these 95 orders, 77 (81.1%) were administered for empiric treatment of suspected Gram-positive infections, 17 (17.9%) were administered for treatment of proven Gram-positive infections (76.5% identified as Staphyloccocus aureus-like agents) and 1 (1.0%) for beta-lactam allergy. The majority of the patients (96.6%) had recently used an antimicrobial medication (3 months). The mean pre-treatment hospitalization period was 11±10 days. Out of the 118 treatments, 67 (56.8%) were for nosocomial infections. The more frequent indications for vancomycin use were pneumonia (48.3%) and primary sepsis (18.6%), accounting for more than 66% of all treatments. No restriction policy was suggested because vancomycin use was considered adequate in the majority of the treatment cases. The broad empiric use of this antimicrobial was greater than expected in the institution and its use should be revised.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.