text stringlengths 0 4.09k |
|---|
Title: Learning to Order Things |
Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order instances given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two... |
Title: Constructing Conditional Plans by a Theorem-Prover |
Abstract: The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the ou... |
Title: Issues in Stacked Generalization |
Abstract: Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classification tasks ever since the introduction of stacked generalization ... |
Title: Ontology Alignment at the Instance and Schema Level |
Abstract: We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alig... |
Title: Monte Carlo Algorithms for the Partition Function and Information Rates of Two-Dimensional Channels |
Abstract: The paper proposes Monte Carlo algorithms for the computation of the information rate of two-dimensional source/channel models. The focus of the paper is on binary-input channels with constraints on the allowed input configurations. The problem of numerically computing the information rate, and even the noise... |
Title: Manifold embedding for curve registration |
Abstract: We focus on the problem of finding a good representative of a sample of random curves warped from a common pattern f. We first prove that such a problem can be moved onto a manifold framework. Then, we propose an estimation of the common pattern f based on an approximated geodesic distance on a suitable manif... |
Title: Kernel Belief Propagation |
Abstract: We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptio... |
Title: Complexity of and Algorithms for Borda Manipulation |
Abstract: We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximatio... |
Title: PAC learnability under non-atomic measures: a problem by Vidyasagar |
Abstract: In response to a 1997 problem of M. Vidyasagar, we state a criterion for PAC learnability of a concept class $\mathscr C$ under the family of all non-atomic (diffuse) measures on the domain $\Omega$. The uniform Glivenko--Cantelli property with respect to non-atomic measures is no longer a necessary condition... |
Title: Scale-Invariant Local Descriptor for Event Recognition in 1D Sensor Signals |
Abstract: In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals. The time-scale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow ... |
Title: A Philosophical Treatise of Universal Induction |
Abstract: Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently ... |
Title: Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces |
Abstract: It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often too inflexible while nonparametric procedures tend to be non-robust because of insufficient data on these high di... |
Title: Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts |
Abstract: We examine a general multi-factor model for commodity spot prices and futures valuation. We extend the multi-factor long-short model in Schwartz and Smith (2000) and Yan (2002) in two important aspects: firstly we allow for both the long and short term dynamic factors to be mean reverting incorporating stocha... |
Title: Efficient sampling of high-dimensional Gaussian fields: the non-stationary / non-sparse case |
Abstract: This paper is devoted to the problem of sampling Gaussian fields in high dimension. Solutions exist for two specific structures of inverse covariance : sparse and circulant. The proposed approach is valid in a more general case and especially as it emerges in inverse problems. It relies on a perturbation-opti... |
Title: Reconstruction of Fractional Brownian Motion Signals From Its Sparse Samples Based on Compressive Sampling |
Abstract: This paper proposes a new fBm (fractional Brownian motion) interpolation/reconstruction method from partially known samples based on CS (Compressive Sampling). Since 1/f property implies power law decay of the fBm spectrum, the fBm signals should be sparse in frequency domain. This property motivates the adop... |
Title: Recovering the shape of a point cloud in the plane |
Abstract: In this work we deal with the problem of support estimation under shape restrictions. The shape restriction we deal with is an extension of the notion of convexity named alpha-convexity. Instead of assuming, as in the convex case, the existence of a separating hyperplane for each exterior point we assume the ... |
Title: Neural Networks for Emotion Classification |
Abstract: It is argued that for the computer to be able to interact with humans, it needs to have the communication skills of humans. One of these skills is the ability to understand the emotional state of the person. This thesis describes a neural network-based approach for emotion classification. We learn a classifie... |
Title: The Perceptron with Dynamic Margin |
Abstract: The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed... |
Title: Alignment of Microtubule Imagery |
Abstract: This work discusses preliminary work aimed at simulating and visualizing the growth process of a tiny structure inside the cell---the microtubule. Difficulty of recording the process lies in the fact that the tissue preparation method for electronic microscopes is highly destructive to live cells. Here in thi... |
Title: Marginal log-linear parameters for graphical Markov models |
Abstract: Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data. MLL parametrizations under linear constraints induce a wide variety of models, including models defined by conditional independences. We introduce a sub-class of MLL models which correspond to Acyclic Directed Mixed Gr... |
Title: A semiparametric estimation of copula models based on the method of moments |
Abstract: Using the classical estimation method of moments, we propose a new semiparametric estimation procedure for multi-parameter copula models. Consistency and asymptotic normality of the obtained estimators are established. By considering an Archimedean copula model, an extensive simulation study, comparing these ... |
Title: RASID: A Robust WLAN Device-free Passive Motion Detection System |
Abstract: WLAN Device-free passive DfP indoor localization is an emerging technology enabling the localization of entities that do not carry any devices nor participate actively in the localization process using the already installed wireless infrastructure. This technology is useful for a variety of applications such ... |
Title: Reasoning on Interval and Point-based Disjunctive Metric Constraints in Temporal Contexts |
Abstract: We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, ... |
Title: Overcoming Misleads In Logic Programs by Redefining Negation |
Abstract: Negation as failure and incomplete information in logic programs have been studied by many researchers In order to explains HOW a negated conclusion was reached, we introduce and proof a different way for negating facts to overcoming misleads in logic programs. Negating facts can be achieved by asking the use... |
Title: Conditional Quantile Processes based on Series or Many Regressors |
Abstract: Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special case, for perfor... |
Title: A statistical learning algorithm for word segmentation |
Abstract: In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recu... |
Title: Confidence sets for network structure |
Abstract: Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that... |
Title: Incremental Top-k List Comparison Approach to Robust Multi-Structure Model Fitting |
Abstract: Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-$k$ lists) derived from residual sorting information. Our method simult... |
Title: Activity-Based Search for Black-Box Contraint-Programming Solvers |
Abstract: Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search, the idea of using the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search an... |
Title: Classification Loss Function for Parameter Ensembles in Bayesian Hierarchical Models |
Abstract: Parameter ensembles or sets of point estimates constitute one of the cornerstones of modern statistical practice. This is especially the case in Bayesian hierarchical models, where different decision-theoretic frameworks can be deployed to summarize such parameter ensembles. The estimation of these parameter ... |
Title: Approximation properties of certain operator-induced norms on Hilbert spaces |
Abstract: We consider a class of operator-induced norms, acting as finite-dimensional surrogates to the L2 norm, and study their approximation properties over Hilbert subspaces of L2 . The class includes, as a special case, the usual empirical norm encountered, for example, in the context of nonparametric regression in... |
Title: Handwritten Character Recognition of South Indian Scripts: A Review |
Abstract: Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a... |
Title: The BG-simulation for Byzantine Mobile Robots |
Abstract: This paper investigates the task solvability of mobile robot systems subject to Byzantine faults. We first consider the gathering problem, which requires all robots to meet in finite time at a non-predefined location. It is known that the solvability of Byzantine gathering strongly depends on a number of syst... |
Title: ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.