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Title: Development of Hybrid Intelligent Systems and their Applications from Engineering Systems to Complex Systems |
Abstract: In this study, we introduce general frame of MAny Connected Intelligent Particles Systems (MACIPS). Connections and interconnections between particles get a complex behavior of such merely simple system (system in system).Contribution of natural computing, under information granulation theory, are the main to... |
Title: Developing Bayesian Information Entropy-based Techniques for Spatially Explicit Model Assessment |
Abstract: The aim of this paper is to explore and develop advanced spatial Bayesian assessment methods and techniques for land use modeling. The paper provides a comprehensive guide for assessing additional informational entropy value of model predictions at the spatially explicit domain of knowledge, and proposes a fe... |
Title: A chain dictionary method for Word Sense Disambiguation and applications |
Abstract: A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorithm, which exploits the sense definitions in the dictionary directly. Our approach uses the lexical base WordNet for a new algorithm originated in ... |
Title: Manifold Learning: The Price of Normalization |
Abstract: We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consists of Locally Linear Embedding (LLE), Laplacian Eigenmap, Local Tangent Space Alignment (LTSA), Hessian Eigenmaps (HLLE), and Diffu... |
Title: Local Procrustes for Manifold Embedding: A Measure of Embedding Quality and Embedding Algorithms |
Abstract: We present the Procrustes measure, a novel measure based on Procrustes rotation that enables quantitative comparison of the output of manifold-based embedding algorithms (such as LLE (Roweis and Saul, 2000) and Isomap (Tenenbaum et al, 2000)). The measure also serves as a natural tool when choosing dimension-... |
Title: Supervised functional classification: A theoretical remark and some comparisons |
Abstract: The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whos... |
Title: Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives |
Abstract: Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning t... |
Title: Learning Graph Matching |
Abstract: As a fundamental problem in pattern recognition, graph matching has applications in a variety of fields, from computer vision to computational biology. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. Many form... |
Title: Neural networks in 3D medical scan visualization |
Abstract: For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts. These volume datasets contain different materials which are difficult to extract and visual... |
Title: Maximum Likelihood Drift Estimation for Multiscale Diffusions |
Abstract: We study the problem of parameter estimation using maximum likelihood for fast/slow systems of stochastic differential equations. Our aim is to shed light on the problem of model/data mismatch at small scales. We consider two classes of fast/slow problems for which a closed coarse-grained equation for the slo... |
Title: BART: Bayesian additive regression trees |
Abstract: We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regre... |
Title: Fast computation of the median by successive binning |
Abstract: This paper describes a new median algorithm and a median approximation algorithm. The former has O(n) average running time and the latter has O(n) worst-case running time. These algorithms are highly competitive with the standard algorithm when computing the median of a single data set, but are significantly ... |
Title: Information field theory for cosmological perturbation reconstruction and non-linear signal analysis |
Abstract: We develop information field theory (IFT) as a means of Bayesian inference on spatially distributed signals, the information fields. A didactical approach is attempted. Starting from general considerations on the nature of measurements, signals, noise, and their relation to a physical reality, we derive the i... |
Title: Statistical Learning of Arbitrary Computable Classifiers |
Abstract: Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with finite Vapnik-Chervonenkis (VC) dimension. The fundamental quantity of interest is the sample complexity: the number of samples required to learn to a specified level of accuracy. Here we consider learning over ... |
Title: How Is Meaning Grounded in Dictionary Definitions? |
Abstract: Meaning cannot be based on dictionary definitions all the way down: at some point the circularity of definitions must be broken in some way, by grounding the meanings of certain words in sensorimotor categories learned from experience or shaped by evolution. This is the "symbol grounding problem." We introduc... |
Title: Improved testing inference in mixed linear models |
Abstract: Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Oftentimes, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In... |
Title: Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods |
Abstract: Measuring the similarity of short written contexts is a fundamental problem in Natural Language Processing. This article provides a unifying framework by which short context problems can be categorized both by their intended application and proposed solution. The goal is to show that various problems and meth... |
Title: Conceptualization of seeded region growing by pixels aggregation. Part 1: the framework |
Abstract: Adams and Bishop have proposed in 1994 a novel region growing algorithm called seeded region growing by pixels aggregation (SRGPA). This paper introduces a framework to implement an algorithm using SRGPA. This framework is built around two concepts: localization and organization of applied action. This concep... |
Title: Conceptualization of seeded region growing by pixels aggregation. Part 2: how to localize a final partition invariant about the seeded region initialisation order |
Abstract: In the previous paper, we have conceptualized the localization and the organization of seeded region growing by pixels aggregation (SRGPA) but we do not give the issue when there is a collision between two distinct regions during the growing process. In this paper, we propose two implementations to manage two... |
Title: Conceptualization of seeded region growing by pixels aggregation. Part 3: a wide range of algorithms |
Abstract: In the two previous papers of this serie, we have created a library, called Population, dedicated to seeded region growing by pixels aggregation and we have proposed different growing processes to get a partition with or without a boundary region to divide the other regions or to get a partition invariant abo... |
Title: Conceptualization of seeded region growing by pixels aggregation. Part 4: Simple, generic and robust extraction of grains in granular materials obtained by X-ray tomography |
Abstract: This paper proposes a simple, generic and robust method to extract the grains from experimental tridimensionnal images of granular materials obtained by X-ray tomography. This extraction has two steps: segmentation and splitting. For the segmentation step, if there is a sufficient contrast between the differe... |
Title: Use of a Quantum Computer and the Quick Medical Reference To Give an Approximate Diagnosis |
Abstract: The Quick Medical Reference (QMR) is a compendium of statistical knowledge connecting diseases to findings (symptoms). The information in QMR can be represented as a Bayesian network. The inference problem (or, in more medical language, giving a diagnosis) for the QMR is to, given some findings, find the prob... |
Title: Information In The Non-Stationary Case |
Abstract: Information estimates such as the ``direct method'' of Strong et al. (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation st... |
Title: Design, Development and Testing of Underwater Vehicles: ITB Experience |
Abstract: The last decade has witnessed increasing worldwide interest in the research of underwater robotics with particular focus on the area of autonomous underwater vehicles (AUVs). The underwater robotics technology has enabled human to access the depth of the ocean to conduct environmental surveys, resources mappi... |
Title: Linear Parameter Varying Model Identification for Control of Rotorcraft-based UAV |
Abstract: A rotorcraft-based unmanned aerial vehicle exhibits more complex properties compared to its full-size counterparts due to its increased sensitivity to control inputs and disturbances and higher bandwidth of its dynamics. As an aerial vehicle with vertical take-off and landing capability, the helicopter specif... |
Title: High-dimensional additive modeling |
Abstract: We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence prop... |
Title: Agnostically Learning Juntas from Random Walks |
Abstract: We prove that the class of functions g:-1,+1^n -> -1,+1 that only depend on an unknown subset of k<<n variables (so-called k-juntas) is agnostically learnable from a random walk in time polynomial in n, 2^k^2, epsilon^-k, and log(1/delta). In other words, there is an algorithm with the claimed running time th... |
Title: On Sequences with Non-Learnable Subsequences |
Abstract: The remarkable results of Foster and Vohra was a starting point for a series of papers which show that any sequence of outcomes can be learned (with no prior knowledge) using some universal randomized forecasting algorithm and forecast-dependent checking rules. We show that for the class of all computationall... |
Title: Prediction with Expert Advice in Games with Unbounded One-Step Gains |
Abstract: The games of prediction with expert advice are considered in this paper. We present some modification of Kalai and Vempala algorithm of following the perturbed leader for the case of unrestrictedly large one-step gains. We show that in general case the cumulative gain of any probabilistic prediction algorithm... |
Title: Computationally Efficient Estimators for Dimension Reductions Using Stable Random Projections |
Abstract: The method of stable random projections is a tool for efficiently computing the $l_\alpha$ distances using low memory, where $0<\alpha \leq 2$ is a tuning parameter. The method boils down to a statistical estimation task and various estimators have been proposed, based on the geometric mean, the harmonic mean... |
Title: On Approximating the Lp Distances for p>2 |
Abstract: Applications in machine learning and data mining require computing pairwise Lp distances in a data matrix A. For massive high-dimensional data, computing all pairwise distances of A can be infeasible. In fact, even storing A or all pairwise distances of A in the memory may be also infeasible. This paper propo... |
Title: On empirical meaning of randomness with respect to a real parameter |
Abstract: We study the empirical meaning of randomness with respect to a family of probability distributions $P_\theta$, where $\theta$ is a real parameter, using algorithmic randomness theory. In the case when for a computable probability distribution $P_\theta$ an effectively strongly consistent estimate exists, we s... |
Title: The model of quantum evolution |
Abstract: This paper has been withdrawn by the author due to extremely unscientific errors. |
Title: Predicting Regional Classification of Levantine Ivory Sculptures: A Machine Learning Approach |
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