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Abstract: We report (to our knowledge) the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distan... |
Title: Joint Structured Models for Extraction from Overlapping Sources |
Abstract: We consider the problem of jointly training structured models for extraction from sources whose instances enjoy partial overlap. This has important applications like user-driven ad-hoc information extraction on the web. Such applications present new challenges in terms of the number of sources and their arbit... |
Title: Adaptive Bases for Reinforcement Learning |
Abstract: We consider the problem of reinforcement learning using function approximation, where the approximating basis can change dynamically while interacting with the environment. A motivation for such an approach is maximizing the value function fitness to the problem faced. Three errors are considered: approximati... |
Title: Generative and Latent Mean Map Kernels |
Abstract: We introduce two kernels that extend the mean map, which embeds probability measures in Hilbert spaces. The generative mean map kernel (GMMK) is a smooth similarity measure between probabilistic models. The latent mean map kernel (LMMK) generalizes the non-iid formulation of Hilbert space embeddings of empiri... |
Title: Analysis of a Random Forests Model |
Abstract: Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and ... |
Title: Conditional Sampling for Spectrally Discrete Max-Stable Random Fields |
Abstract: Max-stable random fields play a central role in modeling extreme value phenomena. We obtain an explicit formula for the conditional probability in general max-linear models, which include a large class of max-stable random fields. As a consequence, we develop an algorithm for efficient and exact sampling from... |
Title: Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation |
Abstract: This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an it... |
Title: Pattern Alternating Maximization Algorithm for Missing Data in Large P, Small N Problems |
Abstract: We propose a new and computationally efficient algorithm for maximizing the observed log-likelihood for a multivariate normal data matrix with missing values. We show that our procedure based on iteratively regressing the missing on the observed variables, generalizes the standard EM algorithm by alternating ... |
Title: Machine Learning for Galaxy Morphology Classification |
Abstract: In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical ... |
Title: Incremental Sampling-based Algorithms for Optimal Motion Planning |
Abstract: During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown to work well in practice and to possess theoretical guarantees such as probabilistic completeness. However, no theoretical bounds on the quality of the solution obta... |
Title: A Unifying View of Multiple Kernel Learning |
Abstract: Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion ... |
Title: Detecting the Most Unusual Part of Two and Three-dimensional Digital Images |
Abstract: The purpose of this paper is to introduce an algorithm that can detect the most unusual part of a digital image in probabilistic setting. The most unusual part of a given shape is defined as a part of the image that has the maximal distance to all non intersecting shapes with the same form. The method is test... |
Title: Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data |
Abstract: One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. Performance guarantees beco... |
Title: An approach to visualize the course of solving of a research task in humans |
Abstract: A technique to study the dynamics of solving of a research task is suggested. The research task was based on specially developed software Right- Wrong Responder (RWR), with the participants having to reveal the response logic of the program. The participants interacted with the program in the form of a semi-b... |
Title: Informal Concepts in Machines |
Abstract: This paper constructively proves the existence of an effective procedure generating a computable (total) function that is not contained in any given effectively enumerable set of such functions. The proof implies the existence of machines that process informal concepts such as computable (total) functions bey... |
Title: The Production of Probabilistic Entropy in Structure/Action Contingency Relations |
Abstract: Luhmann (1984) defined society as a communication system which is structurally coupled to, but not an aggregate of, human action systems. The communication system is then considered as self-organizing ("autopoietic"), as are human actors. Communication systems can be studied by using Shannon's (1948) mathemat... |
Title: Integrating multiple sources to answer questions in Algebraic Topology |
Abstract: We present in this paper an evolution of a tool from a user interface for a concrete Computer Algebra system for Algebraic Topology (the Kenzo system), to a front-end allowing the interoperability among different sources for computation and deduction. The architecture allows the system not only to interface s... |
Title: Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates |
Abstract: The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error probability of ... |
Title: Active Learning for Hidden Attributes in Networks |
Abstract: In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attr... |
Title: Clustering processes |
Abstract: The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of consistency, and show that simple consistent algorithms exist, under most general non-parametric assumptions. The notion of consisten... |
Title: Randomized hybrid linear modeling by local best-fit flats |
Abstract: The hybrid linear modeling problem is to identify a set of d-dimensional affine sets in a D-dimensional Euclidean space. It arises, for example, in object tracking and structure from motion. The hybrid linear model can be considered as the second simplest (behind linear) manifold model of data. In this paper ... |
Title: A majorization-minimization approach to variable selection using spike and slab priors |
Abstract: We develop a method to carry out MAP estimation for a class of Bayesian regression models in which coefficients are assigned with Gaussian-based spike and slab priors. The objective function in the corresponding optimization problem has a Lagrangian form in that regression coefficients are regularized by a mi... |
Title: A two-step fusion process for multi-criteria decision applied to natural hazards in mountains |
Abstract: Mountain river torrents and snow avalanches generate human and material damages with dramatic consequences. Knowledge about natural phenomenona is often lacking and expertise is required for decision and risk management purposes using multi-disciplinary quantitative or qualitative approaches. Expertise is con... |
Title: The Complex Gaussian Kernel LMS algorithm |
Abstract: Although the real reproducing kernels are used in an increasing number of machine learning problems, complex kernels have not, yet, been used, in spite of their potential interest in applications such as communications. In this work, we focus our attention on the complex gaussian kernel and its possible appli... |
Title: Extension of Wirtinger Calculus in RKH Spaces and the Complex Kernel LMS |
Abstract: Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. However, so far, the emphasis has been on batch techniques. It is only recently, that online adaptive techniques have been considered in the context of signal processing tasks. To the b... |
Title: Multistage Hybrid Arabic/Indian Numeral OCR System |
Abstract: The use of OCR in postal services is not yet universal and there are still many countries that process mail sorting manually. Automated Arabic/Indian numeral Optical Character Recognition (OCR) systems for Postal services are being used in some countries, but still there are errors during the mail sorting pro... |
Title: George Forsythe's last paper |
Abstract: We describe von Neumann's elegant idea for sampling from the exponential distribution, Forsythe's generalization for sampling from a probability distribution whose density has the form exp(-G(x)), where G(x) is easy to compute (e.g. a polynomial), and my refinement of these ideas to give an efficient algorith... |
Title: On Building a Knowledge Base for Stability Theory |
Abstract: A lot of mathematical knowledge has been formalized and stored in repositories by now: different mathematical theorems and theories have been taken into consideration and included in mathematical repositories. Applications more distant from pure mathematics, however --- though based on these theories --- ofte... |
Title: Training linear ranking SVMs in linearithmic time using red-black trees |
Abstract: We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number ... |
Title: An Efficient Vein Pattern-based Recognition System |
Abstract: This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image an... |
Title: ECG Feature Extraction Techniques - A Survey Approach |
Abstract: ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of ... |
Title: Introduction to Graphical Modelling |
Abstract: The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov ra... |
Title: Estimating small moments of data stream in nearly optimal space-time |
Abstract: For each $p \in (0,2]$, we present a randomized algorithm that returns an $\epsilon$-approximation of the $p$th frequency moment of a data stream $F_p = \sum_i = 1^n ^p$. The algorithm requires space $O(\epsilon^-2 \log (mM)(\log n))$ and processes each stream update using time $O((\log n) (\log \epsilon^-1))... |
Title: Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators |
Abstract: Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel s... |
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