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Title: Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms |
Abstract: The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest ... |
Title: Learning an Interactive Segmentation System |
Abstract: Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance ... |
Title: A Model-Based Approach to Predicting Predator-Prey & Friend-Foe Relationships in Ant Colonies |
Abstract: Understanding predator-prey relationships among insects is a challenging task in the domain of insect-colony research. This is due to several factors involved, such as determining whether a particular behavior is the result of a predator-prey interaction, a friend-foe interaction or another kind of interactio... |
Title: Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models |
Abstract: A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to mar... |
Title: The Gaussian Surface Area and Noise Sensitivity of Degree-$d$ Polynomials |
Abstract: We provide asymptotically sharp bounds for the Gaussian surface area and the Gaussian noise sensitivity of polynomial threshold functions. In particular we show that if $f$ is a degree-$d$ polynomial threshold function, then its Gaussian sensitivity at noise rate $\epsilon$ is less than some quantity asymptot... |
Title: Condition Number Analysis of Kernel-based Density Ratio Estimation |
Abstract: The ratio of two probability densities can be used for solving various machine learning tasks such as covariate shift adaptation (importance sampling), outlier detection (likelihood-ratio test), and feature selection (mutual information). Recently, several methods of directly estimating the density ratio have... |
Title: Intrusion Detection In Mobile Ad Hoc Networks Using GA Based Feature Selection |
Abstract: Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. MANETs are highly vulnerable to attacks due to the open medium, dynamically changing network topology and lack of centralized monitoring point. It is importa... |
Title: Multi-valued Action Languages in CLP(FD) |
Abstract: Action description languages, such as A and B, are expressive instruments introduced for formalizing planning domains and planning problem instances. The paper starts by proposing a methodology to encode an action language (with conditional effects and static causal laws), a slight variation of B, using Const... |
Title: Variational Bayesian Inference and Complexity Control for Stochastic Block Models |
Abstract: It is now widely accepted that knowledge can be acquired from networks by clustering their vertices according to connection profiles. Many methods have been proposed and in this paper we concentrate on the Stochastic Block Model (SBM). The clustering of vertices and the estimation of SBM model parameters have... |
Title: Representing human and machine dictionaries in Markup languages |
Abstract: In this chapter we present the main issues in representing machine readable dictionaries in XML, and in particular according to the Text Encoding Dictionary (TEI) guidelines. |
Title: Projection Pursuit through $\Phi$-Divergence Minimisation |
Abstract: Consider a defined density on a set of very large dimension. It is quite difficult to find an estimate of this density from a data set. However, it is possible through a projection pursuit methodology to solve this problem. Touboul's article "Projection Pursuit Through Relative Entropy Minimization", 2009, de... |
Title: Complexity of Propositional Abduction for Restricted Sets of Boolean Functions |
Abstract: Abduction is a fundamental and important form of non-monotonic reasoning. Given a knowledge base explaining how the world behaves it aims at finding an explanation for some observed manifestation. In this paper we focus on propositional abduction, where the knowledge base and the manifestation are represented... |
Title: Notes to Robert et al.: Model criticism informs model choice and model comparison |
Abstract: In their letter to PNAS and a comprehensive set of notes on arXiv [arXiv:0909.5673v2], Christian Robert, Kerrie Mengersen and Carla Chen (RMC) represent our approach to model criticism in situations when the likelihood cannot be computed as a way to "contrast several models with each other". In addition, RMC ... |
Title: Multi-Way, Multi-View Learning |
Abstract: We extend multi-way, multivariate ANOVA-type analysis to cases where one covariate is the view, with features of each view coming from different, high-dimensional domains. The different views are assumed to be connected by having paired samples; this is a common setup in recent bioinformatics experiments, of ... |
Title: On Backtracking in Real-time Heuristic Search |
Abstract: Real-time heuristic search algorithms are suitable for situated agents that need to make their decisions in constant time. Since the original work by Korf nearly two decades ago, numerous extensions have been suggested. One of the most intriguing extensions is the idea of backtracking wherein the agent decide... |
Title: Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes |
Abstract: Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels... |
Title: Composite Binary Losses |
Abstract: We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function. We characterise when margin losses can be proper composite losses, explicitly show how... |
Title: New Generalization Bounds for Learning Kernels |
Abstract: This paper presents several novel generalization bounds for the problem of learning kernels based on the analysis of the Rademacher complexity of the corresponding hypothesis sets. Our bound for learning kernels with a convex combination of p base kernels has only a log(p) dependency on the number of kernels,... |
Title: Optimal construction of k-nearest neighbor graphs for identifying noisy clusters |
Abstract: We study clustering algorithms based on neighborhood graphs on a random sample of data points. The question we ask is how such a graph should be constructed in order to obtain optimal clustering results. Which type of neighborhood graph should one choose, mutual k-nearest neighbor or symmetric k-nearest neigh... |
Title: Matching 2-D Ellipses to 3-D Circles with Application to Vehicle Pose Estimation |
Abstract: Finding the three-dimensional representation of all or a part of a scene from a single two dimensional image is a challenging task. In this paper we propose a method for identifying the pose and location of objects with circular protrusions in three dimensions from a single image and a 3d representation or mo... |
Title: A Geometric Proof of Calibration |
Abstract: We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff fun... |
Title: Geometric Representations of Random Hypergraphs |
Abstract: A parametrization of hypergraphs based on the geometry of points in $^d$ is developed. Informative prior distributions on hypergraphs are induced through this parametrization by priors on point configurations via spatial processes. This prior specification is used to infer conditional independence models or M... |
Title: A Survey of Paraphrasing and Textual Entailment Methods |
Abstract: Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts... |
Title: On the de la Garza Phenomenon |
Abstract: Deriving optimal designs for nonlinear models is in general challenging. One crucial step is to determine the number of support points needed. Current tools handle this on a case-by-case basis. Each combination of model, optimality criterion and objective requires its own proof. The celebrated de la Garza Phe... |
Title: P values, confidence intervals, or confidence levels for hypotheses? |
Abstract: Null hypothesis significance tests and p values are widely used despite very strong arguments against their use in many contexts. Confidence intervals are often recommended as an alternative, but these do not achieve the objective of assessing the credibility of a hypothesis, and the distinction between confi... |
Title: Bootstrapping Confidence Levels for Hypotheses about Quadratic (U-Shaped) Regression Models |
Abstract: Bootstrapping can produce confidence levels for hypotheses about quadratic regression models - such as whether the U-shape is inverted, and the location of optima. The method has several advantages over conventional methods: it provides more, and clearer, information, and is flexible - it could easily be appl... |
Title: Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling |
Abstract: When dealing with very large datasets of functional data, survey sampling approaches are useful in order to obtain estimators of simple functional quantities, without being obliged to store all the data. We propose here a Horvitz--Thompson estimator of the mean trajectory. In the context of a superpopulation ... |
Title: Speech Recognition Oriented Vowel Classification Using Temporal Radial Basis Functions |
Abstract: The recent resurgence of interest in spatio-temporal neural network as speech recognition tool motivates the present investigation. In this paper an approach was developed based on temporal radial basis function "TRBF" looking to many advantages: few parameters, speed convergence and time invariance. This app... |
Title: Modeling and Application of Series Elastic Actuators for Force Control Multi Legged Robots |
Abstract: Series Elastic Actuators provide many benefits in force control of robots in unconstrained environments. These benefits include high force fidelity, extremely low impedance, low friction, and good force control bandwidth. Series Elastic Actuators employ a novel mechanical design architecture which goes agains... |
Title: A Novel Feature Extraction for Robust EMG Pattern Recognition |
Abstract: Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering an... |
Title: Performance Analysis of AIM-K-means & K-means in Quality Cluster Generation |
Abstract: Among all the partition based clustering algorithms K-means is the most popular and well known method. It generally shows impressive results even in considerably large data sets. The computational complexity of K-means does not suffer from the size of the data set. The main disadvantage faced in performing th... |
Title: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design |
Abstract: Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low RKHS norm. We resolve the important open problem of deriving regret bounds ... |
Title: Restricted Eigenvalue Conditions on Subgaussian Random Matrices |
Abstract: It is natural to ask: what kinds of matrices satisfy the Restricted Eigenvalue (RE) condition? In this paper, we associate the RE condition (Bickel-Ritov-Tsybakov 09) with the complexity of a subset of the sphere in $\R^p$, where $p$ is the dimensionality of the data, and show that a class of random matrices ... |
Title: The assessment and planning of non-inferiority trials for retention of effect hypotheses - towards a general approach |
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