A hyperspectral image is characterized by a large dimensionality data, recorded at very fine spectral resolution in hundreds of narrow frequency bands. These bands provide a wealth of spatial and spectral information of the scene, imaged by the sensors. Histogram binning and morphological operator based Extreme Learning Machine classifier is proposed. Histogram binning and morphological operato...

Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are wellcalibrated, ab initio. The other approach is to use some post-processing methods for transforming the ou...

A new solution method to the Nearest Neighbour Problem is presented. The method is based upon the triangle inequality and works well for small point sets, where traditional solutions are particularly ineffective. Its performance is characterized experimentally and compared with k-d tree and Elias approaches. A hybrid approach is proposed wherein the triangle inequality method is applied to the ...

Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods. The method post processes the output of a binary classification algori...

The creation of a PKI with trusted roots on a X.509 infrastructure has solved the problem of key exchange and enabled widespread use of encryption between individuals with no previous contact. However, these certificates are inadequate for making a “trust or do not trust” decision in web interactions as exemplified by MITM attacks, phishing attacks, and rogue but technically valid certificates....

Gillespie’s Stochastic Simulation Algorithm (SSA) is an exact procedure for simulating the evolution of a collection of discrete, interacting entities, such as coalescing aerosol particles or reacting chemical species. The high computational cost of SSA has motivated the development of more efficient variants, such as Tau-Leaping, which sacrifices the exactness of SSA. For models whose interact...

Faster likelihood evaluation enhances the efficiency of gravitational wave signal analysis. We present Mode-by-mode Relative Binning (MRB), a new method designed for obtaining fast and accurate likelihoods advanced waveform models that include spin-orbit precession effects multiple radiation harmonics from compact binary coalescence. Leveraging "twisting-up" procedure constructing precessing mo...

The reliability diagram is a common diagnostic graph used to summarise and evaluate probabilistic forecasts. Its strengths lie in the ease with which it is produced and the transparency of its definition. While visually appealing, major long noted shortcomings lie in the difficulty of interpreting the graph visually; for the most part, ambiguities arise from variation in the distribution of for...

Starting from the radiative transport equation we derive the scaling relationships that enable a single Monte Carlo (MC) simulation to predict the spatially- and temporally-resolved reflectance from homogeneous semi-infinite media with arbitrary scattering and absorption coefficients. This derivation shows that a rigorous application of this single Monte Carlo (sMC) approach requires the rescal...

In this study a new approach to generate association rules on numeric data is proposed. It has been observed that equal binning techniques are not always useful to convert numerical data into categorical data, specifically in medical data. The proposed approach utilise a modified equal width binning interval technique to discretise continuous valued attributes to nominal based on opinion taken ...