Classification of reduction invariants with improved backpropagation

Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal component analysis (PCA), factor analysis, and feature clustering. Principal components are extracted from a collection of multivariate cases as a way of accounting for as much of the variation in that collection as possible by means of as few variables as possible. On the other hand, backpropagation network has been used extensively in classification problems such as XOR problems, share prices prediction, and pattern recognition. This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information present in moment invariants of handwritten digits, leaving the redundant information behind. Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification purposes

Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

A practical Bayesian framework for backpropagation networks

A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules for network pruning or growing procedures, (3) objective choice of magnitude and type of weight decay terms or additive regularizers (for penalizing large weights, etc.), (4) a measure of the effective number of well-determined parameters in a model, (5) quantified estimates of the error bars on network parameters and on network output, and (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian "evidence" automatically embodies "Occam's razor," penalizing overflexible and overcomplex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalization ability and the Bayesian evidence is obtained

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods