Universal approximation propriety of Flexible Beta Basis Function Neural Tree

Abstract — In this paper, the universal approximation propri

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Predicting the Effectiveness of Medical Interventions

This dissertation explores several conceptual and methodological features of medical science that influence our ability to accurately predict medical effectiveness. Making reliable predictions about the effectiveness of medical treatments is crucial to mitigating death and disease and improving individual and population health, yet generating such predictions is fraught with difficulties. Each chapter deals with a unique challenge to predictions of medical effectiveness. In Chapter 1, I describe and analyze the principles underlying three prominent approaches to physical disease classification—the etiological, symptom-based, and pathophysiological models—and suggest a broadly pragmatic approach whereby appropriate classifications depend on the goal in question. In line with this, I argue that particular features of the pathophysiological model, such as its focus on disease mechanisms, make it most relevant for predicting medical effectiveness. Chapter 2 explores the debate between those who argue that statistical evidence is sufficient for inferring medical effectiveness and those who argue that we require both statistical and mechanistic evidence. I focus on the question of how mechanistic and statistical evidence can be integrated. I highlight some of the challenges facing formal techniques, such as Bayesian networks, and use Toulmin’s model of argumentation to offer a complementary model of evidence amalgamation, which allows for the systematic integration of statistical and mechanistic evidence. In Chapter 3, I focus on p-hacking, an application of analytic techniques that may lead to exaggerated experimental results. I use philosophical tools from decision theory to illustrate how severe the effects of p-hacking can be. While it is typically considered epistemically questionable and practically harmful, I appeal to the argument from inductive risk to defend the view that there are some contexts in which p-hacking may be warranted. Chapter 4 draws attention to a particular set of biases plaguing medical research: Meta-biases. I argue that biases of this type, such as publication bias and sponsorship bias, lead to exaggerated clinical trial results. I then offer a framework, the bias dynamics model, that corrects for the influence of meta-biases on estimations of medical effectiveness. In Chapter 5, I argue against the prominent view that AI models are not explainable by showing how four familiar accounts of scientific explanation can be applied to neural networks. The confusion about explaining AI models is due to the conflation of ‘explainability’, ‘understandability’, and ‘interpretability’. To remedy this, I offer a novel account of AI-interpretability, according to which an interpretation is something one does to an explanation with the explicit aim of producing another, more understandable, explanation.The Oppenheimer Memorial Trust Department of History and Philosophy of Science, Cambridge Universit

Modelling and predicting distribution-valued fields with applications to inversion under uncertainty

Capturing the dependence between a random response and predictors is a fundamental task in statistics and stochastic modelling. The focus of this work is on density regression, which entails estimating response distributions given predictor values. It enables the derivation of various statistical quantities, including the conditional mean, threshold exceedance probabilities, and quantiles. This thesis presents a flexible approach, based upon the class of so-called Spatial Logistic Gaussian Processes (SLGPs). The SLGP framework utilizes a well-behaved latent Gaussian Process that undergoes a non-linear transformation, resulting in a class of models suitable for capturing spatially-dependent probability measures. SLGP models overcome limitations associated with strong distributional assumptions (e.g. shapes constraints, log-concavity, Gaussianity, etc.), varying sample sizes, and changes in target density shapes and modalities. The first part of this work is dedicated to the development of SLGP models and gaining a deep understanding of the associated mathematical concepts. We introduce SLGPs from the perspective of random measures and their densities, and investigate links between properties of SLGPs and underlying processes. We show that SLGP models can be characterized by their log-increments and leverage this characterization to establish theoretical results with a main focus on spatial regularity. We then focus on applicability of our approach, and propose an implementation relying on finite rank Gaussian Processes. We demonstrate it on synthetic examples and on temperature distributions at meteorological stations. Finally, we address the potential of SLGPs for statistical inference, focusing on their potential in stochastic optimization and stochastic inverse problems. Notably, for inverse problems, an Approximate Bayesian Computation (ABC) framework is introduced, leveraging SLGP-surrogated likelihoods to accommodate situations with limited to moderate data. This methodology, inspired by GP-ABC methods, harnesses the probabilistic nature of SLGPs to guide data acquisition, thereby facilitating accelerated inference. We illustrate these approaches on synthetic examples as well as on a hydrogeological inverse problem in which a contaminant source is sought under uncertain geological scenario

PSA 2016

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio