Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

PhotoVoltaic DC-DC module integrated converter for novel cascaded and bypass grid connection topologies - design and optimisation

Grid connected PhotoVoltaic (PV) inverters fall into three broad categories — Central, String and Module Integrated Converers (MICs). MICs offer any avantaes in performance and flexibility, but are at a cost disadvantage. Two alternative novel approaches proposed by the author — cascaded dc-dc MICs and bypass dc-dc MICs — integrate a simple non-isolated intelligent dc-dc converter with each PV module to provide the advantages of dc-ac MICs at a lower cost. A suitable universal 150W 5A dc-dc converter design is presented based on two interleaved MOSFET half bridges. Testing shows Zero Voltage Switching (ZVS) keeps losses under 1W for bi-directional power flows up to 15W between two adjacent 12V PV modules for the bypass application, and efficiencies over 94% for most of the operational power range for the cascaded converter application. Based on the experimental results, potential optimizations to further reduce losses are discussed.

Cascaded DC-DC converter connection of photovoltaic modules

New residential scale photovoltaic (PV) arrays are commonly connected to the grid by a single DC-AC inverter connected to a series string of PV modules, or many small DC-AC inverters which connect one or two modules directly to the AC grid. This paper shows that a "converter-per-module" approach offers many advantages including individual module maximum power point tracking, which gives great flexibility in module layout, replacement, and insensitivity to shading; better protection of PV sources, and redundancy in the case of source or converter failure; easier and safer installation and maintenance; and better data gathering. Simple nonisolated per-module DC-DC converters can be series connected to create a high voltage string connected to a simplified DC-AC inverter. These advantages are available without the cost or efficiency penalties of individual DC-AC grid connected inverters. Buck, boost, buck-boost and Cuk converters are possible cascadable converters. The boost converter is best if a significant step up is required, such as with a short string of 12 PV modules. A string of buck converters requires many more modules, but can always deliver any combination of module power. The buck converter is the most efficient topology for a given cost. While flexible in voltage ranges, buck-boost and Cuk converters are always at an efficiency or alternatively cost disadvantage.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

The n-tuple classifier: too good to ignore

The n-tuple pattern recognition method has been tested using a selection of 11 large data sets from the European Community StatLog project, so that the results could be compared with those reported for the 23 other algorithms the project tested. The results indicate that this ultra-fast memory-based method is a viable competitor with the others, which include optimisation-based neural network algorithms, even though the theory of memory-based neural computing is less highly developed in terms of statistical theory.

Benchmarking the n-tuple classifier with statlog dataset

The n-tuple recognition method was tested on 11 large real-world data sets and its performance compared to 23 other classification algorithms. On 7 of these, the results show no systematic performance gap between the n-tuple method and the others. Evidence was found to support a possible explanation for why the n-tuple method yields poor results for certain datasets. Preliminary empirical results of a study of the confidence interval (the difference between the two highest scores) are also reported. These suggest a counter-intuitive correlation between the confidence interval distribution and the overall classification performance of the system.

Good-turing estimation for the frequentist n-tuple classifier

We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.

A theoretical and experimental account of n-tuple classifier performance

The n-tuple recognition method is briefly reviewed, summarizing the main theoretical results. Large-scale experiments carried out on Stat-Log project datasets confirm this method as a viable competitor to more popular methods due to its speed, simplicity, and accuracy on the majority of a wide variety of classification problems. A further investigation into the failure of the method on certain datasets finds the problem to be largely due to a mismatch between the scales which describe generalization and data sparseness.

Cascaded Bayesian processes: an account of bias in orientation perception

Following adaptation to an oriented (1-d) signal in central vision, the orientation of subsequently viewed test signals may appear repelled away from or attracted towards the adapting orientation. Small angular differences between the adaptor and test yield 'repulsive' shifts, while large angular differences yield 'attractive' shifts. In peripheral vision, however, both small and large angular differences yield repulsive shifts. To account for these tilt after-effects (TAEs), a cascaded model of orientation estimation that is optimized using hierarchical Bayesian methods is proposed. The model accounts for orientation bias through adaptation-induced losses in information that arise because of signal uncertainties and neural constraints placed upon the propagation of visual information. Repulsive (direct) TAEs arise at early stages of visual processing from adaptation of orientation-selective units with peak sensitivity at the orientation of the adaptor (theta). Attractive (indirect) TAEs result from adaptation of second-stage units with peak sensitivity at theta and theta+90 degrees , which arise from an efficient stage of linear compression that pools across the responses of the first-stage orientation-selective units. A spatial orientation vector is estimated from the transformed oriented unit responses. The change from attractive to repulsive TAEs in peripheral vision can be explained by the differing harmonic biases resulting from constraints on signal power (in central vision) versus signal uncertainties in orientation (in peripheral vision). The proposed model is consistent with recent work by computational neuroscientists in supposing that visual bias reflects the adjustment of a rational system in the light of uncertain signals and system constraints.

Soliton switching using cascaded nonlinear-optical loop mirrors

We demonstrate multiple-peaked switching in a nonlinear-optical loop mirror and present an experimental investigation of device cascading in the soliton regime based on a sequence of two independent nonlinear-optical loop mirrors. Cascading leads to an enhanced switching response with sharper switching edges, flattened peaks, and increased interpeak extinction ratios. We observe that pulses emerging from the cascade retain the sech2 temporal profile of a soliton with minimal degradation in the spectral characteristics.

Simultaneous refractive index and temperature measurement using cascaded long-period grating in double-cladding fibre

A compact scheme for simultaneous temperature and surrounding refractive index (SRI) measurement using two long-period gratings (LPGs) of different periods inscribed side-by-side in a single piece of a double-cladding fibre is presented. One of the LPGs is sensitive to both SRI and temperature, whilst the second is sensitive to temperature only.

Cascaded nonlinear absorption of femtosecond laser pulses in dielectrics

We investigated the energy deposition process leading to the waveguide inscription in transparent dielectrics both experimentally and theoretically. Parameters of multiphoton absorption process and inscription thresholds were measured in a range of materials including YAG, ZnSe, RbPb2Cl5 crystals, and in fused silica and BK7 glasses.

Simultaneous refractive index and temperature measurement using a cascaded long-period grating device

We present a compact scheme for simultaneous temperature and surrounding refractive index (SRI) measurement using two long period gratings (LPGs) of different periods inscribed side-by-side in a single piece of a double-cladding fibre. One of the LPGs is sensitive to both SRI and temperature changes whilst the second is SRI-insensitive but shows spectral shift with temperature changes. In addition, we show that a resonance peak of the SRI-insensitive LPG can be designed to appear in the EDFA wavelength region with potential use for gain flattening applications.

Optimal packing for cascaded regenerative transmission based on phase sensitive amplifiers

We investigate the transmission performance of advanced modulation formats in nonlinear regenerative channels based on cascaded phase sensitive amplifiers. We identify the impact of amplitude and phase noise dynamics along the transmission line and show that after a cascade of regenerators, densely packed single ring PSK constellations outperform multi-ring constellations. The results of this study will greatly simplify the design of future nonlinear regenerative channels for ultra-high capacity transmission