text stringlengths 0 4.09k |
|---|
Title: Learning nonsingular phylogenies and hidden Markov models Abstract: In this paper we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all transition matrices have determinants bounded away from 0 (and 1). We highlight the role of the nonsingularity conditi... |
Title: The Self-Organization of Speech Sounds Abstract: The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existe... |
Title: On Generalized Computable Universal Priors and their Convergence Abstract: Solomonoff unified Occam's razor and Epicurus' principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field of algorithmic information theory. His central result is that the... |
Title: Consistency in Models for Distributed Learning under Communication Constraints Abstract: Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition by allocating observations of an i... |
Title: Distributed Learning in Wireless Sensor Networks Abstract: The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of... |
Title: Probabilistic and Team PFIN-type Learning: General Properties Abstract: We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answer... |
Title: The Bayesian Decision Tree Technique with a Sweeping Strategy Abstract: The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a ... |
Title: Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles Abstract: In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a syntheti... |
Title: Learning Multi-Class Neural-Network Models from Electroencephalograms Abstract: We describe a new algorithm for learning multi-class neural-network models from large-scale clinical electroencephalograms (EEGs). This algorithm trains hidden neurons separately to classify all the pairs of classes. To find best pai... |
Title: Learning from Web: Review of Approaches Abstract: Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction from web documents deals with unstructured, free-format documents whose number is enormous and rapidl... |
Title: Selection in Scale-Free Small World Abstract: In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web. The selection algorit... |
Title: A Neural-Network Technique to Learn Concepts from Electroencephalograms Abstract: A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In th... |
Title: The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns Abstract: In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms... |
Title: Adaptive Online Prediction by Following the Perturbed Leader Abstract: When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority derivatives quite complicated. In... |
Title: Componentwise Least Squares Support Vector Machines Abstract: This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additi... |
Title: A linear memory algorithm for Baum-Welch training Abstract: Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. Methods and results: We introduce a linear space algorithm for Baum-Welch t... |
Title: Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks Abstract: A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks... |
Title: Defensive forecasting Abstract: We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as f... |
Title: Non-asymptotic calibration and resolution Abstract: We analyze a new algorithm for probability forecasting of binary observations on the basis of the available data, without making any assumptions about the way the observations are generated. The algorithm is shown to be well calibrated and to have good resoluti... |
Title: Defensive forecasting for linear protocols Abstract: We consider a general class of forecasting protocols, called "linear protocols", and discuss several important special cases, including multi-class forecasting. Forecasting is formalized as a game between three players: Reality, whose role is to generate obser... |
Title: Asymptotics of Discrete MDL for Online Prediction Abstract: Minimum Description Length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning non-i.i.d. processes by means of two-part MDL, where the underlying model class i... |
Title: Competitive on-line learning with a convex loss function Abstract: We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on the following binary observation. In competitive on-line learning, the goal is to design decision algorithms that are almost... |
Title: About one 3-parameter Model of Testing Abstract: This article offers a 3-parameter model of testing, with 1) the difference between the ability level of the examinee and item difficulty; 2) the examinee discrimination and 3) the item discrimination as model parameters. |
Title: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales Abstract: We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's eva... |
Title: On the Job Training Abstract: We propose a new framework for building and evaluating machine learning algorithms. We argue that many real-world problems require an agent which must quickly learn to respond to demands, yet can continue to perform and respond to new training throughout its useful life. We give a f... |
Title: Deriving a Stationary Dynamic Bayesian Network from a Logic Program with Recursive Loops Abstract: Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they gener... |
Title: Efficient Multiclass Implementations of L1-Regularized Maximum Entropy Abstract: This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditi... |
Title: Multiresolution Kernels Abstract: We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures and takes advantage of a more detailed "b... |
Title: Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections Abstract: Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized esti... |
Title: Pattern Recognition for Conditionally Independent Data Abstract: In this work we consider the task of relaxing the i.i.d assumption in pattern recognition (or classification), aiming to make existing learning algorithms applicable to a wider range of tasks. Pattern recognition is guessing a discrete label of som... |
Title: Monotone Conditional Complexity Bounds on Future Prediction Errors Abstract: We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true distribution m by the algorithmic complexity of m. Here we ... |
Title: Defensive Universal Learning with Experts Abstract: This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with co... |
Title: FPL Analysis for Adaptive Bandits Abstract: A main problem of "Follow the Perturbed Leader" strategies for online decision problems is that regret bounds are typically proven against oblivious adversary. In partial observation cases, it was not clear how to obtain performance guarantees against adaptive adversar... |
Title: Regularity of Position Sequences Abstract: A person is given a numbered sequence of positions on a sheet of paper. The person is asked, "Which will be the next (or the next after that) position?" Everyone has an opinion as to how he or she would proceed. There are regular sequences for which there is general agr... |
Title: Expectation maximization as message passing Abstract: Based on prior work by Eckford, it is shown how expectation maximization (EM) may be viewed, and used, as a message passing algorithm in factor graphs. |
Title: Sequential Predictions based on Algorithmic Complexity Abstract: This paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's universal prior M, the latter being an excellent predictor in det... |
Title: Measuring Semantic Similarity by Latent Relational Analysis Abstract: This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they ... |
Title: Universal Learning of Repeated Matrix Games Abstract: We study and compare the learning dynamics of two universal learning algorithms, one based on Bayesian learning and the other on prediction with expert advice. Both approaches have strong asymptotic performance guarantees. When confronted with the task of fin... |
Title: Corpus-based Learning of Analogies and Semantic Relations Abstract: We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A... |
Title: Learning Optimal Augmented Bayes Networks Abstract: Naive Bayes is a simple Bayesian classifier with strong independence assumptions among the attributes. This classifier, desipte its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of a ... |
Title: Learning Unions of $\omega(1)$-Dimensional Rectangles Abstract: We consider the problem of learning unions of rectangles over the domain $[b]^n$, in the uniform distribution membership query learning setting, where both b and n are "large". We obtain poly$(n, \log b)$-time algorithms for the following classes: -... |
Title: When Ignorance is Bliss Abstract: It is commonly-accepted wisdom that more information is better, and that information should never be ignored. Here we argue, using both a Bayesian and a non-Bayesian analysis, that in some situations you are better off ignoring information if your uncertainty is represented by a... |
Title: The Impact of Social Networks on Multi-Agent Recommender Systems Abstract: Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to have agents sample products at random while randomly querying one another for the best item they have found; we improve upon this by adding a communication network... |
Title: On-line regression competitive with reproducing kernel Hilbert spaces Abstract: We consider the problem of on-line prediction of real-valued labels, assumed bounded in absolute value by a known constant, of new objects from known labeled objects. The prediction algorithm's performance is measured by the squared ... |
Title: Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data Abstract: Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of ... |
Title: Robust Inference of Trees Abstract: This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutu... |
Title: Bounds on Query Convergence Abstract: The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many contexts, including economics, business, medicine, experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t - x*)^2 ] >= O(1/sqrt(t)) on the rate of conver... |
Title: The Signed Distance Function: A New Tool for Binary Classification Abstract: From a geometric perspective most nonlinear binary classification algorithms, including state of the art versions of Support Vector Machine (SVM) and Radial Basis Function Network (RBFN) classifiers, and are based on the idea of reconst... |
Title: Parameter Estimation of Hidden Diffusion Processes: Particle Filter vs. Modified Baum-Welch Algorithm Abstract: We propose a new method for the estimation of parameters of hidden diffusion processes. Based on parametrization of the transition matrix, the Baum-Welch algorithm is improved. The algorithm is compare... |
Title: Joint fixed-rate universal lossy coding and identification of continuous-alphabet memoryless sources Abstract: The problem of joint universal source coding and identification is considered in the setting of fixed-rate lossy coding of continuous-alphabet memoryless sources. For a wide class of bounded distortion ... |
Title: DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework Abstract: In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementation... |
Title: Preference Learning in Terminology Extraction: A ROC-based approach Abstract: A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. In this paper, the task of extracting relevant collocations is achieved through a supervised learnin... |
Title: Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower Bounds for Hard Sets Abstract: We establish a relationship between the online mistake-bound model of learning and resource-bounded dimension. This connection is combined with the Winnow algorithm to obtain new results about the density of ha... |
Title: Competing with wild prediction rules Abstract: We consider the problem of on-line prediction competitive with a benchmark class of continuous but highly irregular prediction rules. It is known that if the benchmark class is a reproducing kernel Hilbert space, there exists a prediction algorithm whose average los... |
Title: Complex Random Vectors and ICA Models: Identifiability, Uniqueness and Separability Abstract: In this paper the conditions for identifiability, separability and uniqueness of linear complex valued independent component analysis (ICA) models are established. These results extend the well-known conditions for solv... |
Title: Genetic Programming, Validation Sets, and Parsimony Pressure Abstract: Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited number of samples. This paper is an investigation on two method... |
Title: Joint universal lossy coding and identification of i.i.d. vector sources Abstract: The problem of joint universal source coding and modeling, addressed by Rissanen in the context of lossless codes, is generalized to fixed-rate lossy coding of continuous-alphabet memoryless sources. We show that, for bounded dist... |
Title: Processing of Test Matrices with Guessing Correction Abstract: It is suggested to insert into test matrix 1s for correct responses, 0s for response refusals, and negative corrective elements for incorrect responses. With the classical test theory approach test scores of examinees and items are calculated traditi... |
Title: Distributed Kernel Regression: An Algorithm for Training Collaboratively Abstract: This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a gener... |
Title: Decision Making with Side Information and Unbounded Loss Functions Abstract: We consider the problem of decision-making with side information and unbounded loss functions. Inspired by probably approximately correct learning model, we use a slightly different model that incorporates the notion of side information... |
Title: How to Beat the Adaptive Multi-Armed Bandit Abstract: The multi-armed bandit is a concise model for the problem of iterated decision-making under uncertainty. In each round, a gambler must pull one of $K$ arms of a slot machine, without any foreknowledge of their payouts, except that they are uniformly bounded. ... |
Title: Learning rational stochastic languages Abstract: Given a finite set of words w1,...,wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference is to infer an estimate of P in some class of probabilistic models, such as Probabilistic... |
Title: Inconsistent parameter estimation in Markov random fields: Benefits in the computation-limited setting Abstract: Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and then the fitted model i... |
Title: Rational stochastic languages Abstract: The goal of the present paper is to provide a systematic and comprehensive study of rational stochastic languages over a semiring K \in {Q, Q +, R, R+}. A rational stochastic language is a probability distribution over a free monoid \Sigma^* which is rational over K, that ... |
Title: Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot Abstract: We address the problem of autonomously learning controllers for vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories. We demonst... |
Title: Topological Grammars for Data Approximation Abstract: A method of {\it topological grammars} is proposed for multidimensional data approximation. For data with complex topology we define a {\it principal cubic complex} of low dimension and given complexity that gives the best approximation for the dataset. This ... |
Title: Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence Abstract: We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an agent is to attain the best possible asymptotic rew... |
Title: Nearly optimal exploration-exploitation decision thresholds Abstract: While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. In this paper, we first derive upper bounds for the utility of selecting different actions ... |
Title: Semi-Supervised Learning -- A Statistical Physics Approach Abstract: We present a novel approach to semi-supervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the points by minimizing a certain energy function, which corresponds to ... |
Title: Revealing the Autonomous System Taxonomy: The Machine Learning Approach Abstract: Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or sma... |
Title: Concerning the differentiability of the energy function in vector quantization algorithms Abstract: The adaptation rule for Vector Quantization algorithms, and consequently the convergence of the generated sequence, depends on the existence and properties of a function called the energy function, defined on a to... |
Title: HCI and Educational Metrics as Tools for VLE Evaluation Abstract: The general set of HCI and Educational principles are considered and a classification system constructed. A frequency analysis of principles is used to obtain the most significant set. Metrics are devised to provide objective measures of these pri... |
Title: On the Foundations of Universal Sequence Prediction Abstract: Solomonoff completed the Bayesian framework by providing a rigorous, unique, formal, and universal choice for the model class and the prior. We discuss in breadth how and in which sense universal (non-i.i.d.) sequence prediction solves various (philos... |
Title: A Formal Measure of Machine Intelligence Abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in ... |
Title: Query Chains: Learning to Rank from Implicit Feedback Abstract: This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Usin... |
Title: Evaluating the Robustness of Learning from Implicit Feedback Abstract: This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory and real-world settings. The model is used to understand the ef... |
Title: Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs Abstract: Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a us... |
Title: General Discounting versus Average Reward Abstract: Consider an agent interacting with an environment in cycles. In every interaction cycle the agent is rewarded for its performance. We compare the average reward U from cycle 1 to m (average value) with the future discounted reward V from cycle k to infinity (di... |
Title: On Learning Thresholds of Parities and Unions of Rectangles in Random Walk Models Abstract: In a recent breakthrough, [Bshouty et al., 2005] obtained the first passive-learning algorithm for DNFs under the uniform distribution. They showed that DNFs are learnable in the Random Walk and Noise Sensitivity models. ... |
Title: On Sequence Prediction for Arbitrary Measures Abstract: Suppose we are given two probability measures on the set of one-way infinite finite-alphabet sequences and consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense) when one of t... |
Title: Predictions as statements and decisions Abstract: Prediction is a complex notion, and different predictors (such as people, computer programs, and probabilistic theories) can pursue very different goals. In this paper I will review some popular kinds of prediction and argue that the theory of competitive on-line... |
Title: The generating function of the polytope of transport matrices $U(r,c)$ as a positive semidefinite kernel of the marginals $r$ and $c$ Abstract: This paper has been withdrawn by the author due to a crucial error in the proof of Lemma 5. |
Title: PAC Classification based on PAC Estimates of Label Class Distributions Abstract: A standard approach in pattern classification is to estimate the distributions of the label classes, and then to apply the Bayes classifier to the estimates of the distributions in order to classify unlabeled examples. As one might ... |
Title: Competing with stationary prediction strategies Abstract: In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptio... |
Title: Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference Abstract: In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some class of probabilistic models such as proba... |
Title: Logical settings for concept learning from incomplete examples in First Order Logic Abstract: We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with data incompleteness. More precisely we ar... |
Title: A Theory of Probabilistic Boosting, Decision Trees and Matryoshki Abstract: We present a theory of boosting probabilistic classifiers. We place ourselves in the situation of a user who only provides a stopping parameter and a probabilistic weak learner/classifier and compare three types of boosting algorithms: p... |
Title: Expressing Implicit Semantic Relations without Supervision Abstract: We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patter... |
Title: Leading strategies in competitive on-line prediction Abstract: We start from a simple asymptotic result for the problem of on-line regression with the quadratic loss function: the class of continuous limited-memory prediction strategies admits a "leading prediction strategy", which not only asymptotically perfor... |
Title: Competing with Markov prediction strategies Abstract: Assuming that the loss function is convex in the prediction, we construct a prediction strategy universal for the class of Markov prediction strategies, not necessarily continuous. Allowing randomization, we remove the requirement of convexity. |
Title: A Foundation to Perception Computing, Logic and Automata Abstract: In this report, a novel approach to intelligence and learning is introduced, this approach is based on what we call 'perception logic'. Based on this logic, a computing mechanism and automata are introduced. Multi-resolution analysis of perceptua... |
Title: A Study on Learnability for Rigid Lambek Grammars Abstract: We present basic notions of Gold's "learnability in the limit" paradigm, first presented in 1967, a formalization of the cognitive process by which a native speaker gets to grasp the underlying grammar of his/her own native language by being exposed to ... |
Title: Similarity of Semantic Relations Abstract: There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them syno... |
Title: A Massive Local Rules Search Approach to the Classification Problem Abstract: An approach to the classification problem of machine learning, based on building local classification rules, is developed. The local rules are considered as projections of the global classification rules to the event we want to classif... |
Title: Metric entropy in competitive on-line prediction Abstract: Competitive on-line prediction (also known as universal prediction of individual sequences) is a strand of learning theory avoiding making any stochastic assumptions about the way the observations are generated. The predictor's goal is to compete with a ... |
Title: Scanning and Sequential Decision Making for Multi-Dimensional Data - Part I: the Noiseless Case Abstract: We investigate the problem of scanning and prediction ("scandiction", for short) of multidimensional data arrays. This problem arises in several aspects of image and video processing, such as predictive codi... |
Title: A kernel method for canonical correlation analysis Abstract: Canonical correlation analysis is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support ve... |
Title: PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation Assumption Abstract: We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kearns et al. Here the task is to construct a hypothesis mi... |
Title: Motion Primitives for Robotic Flight Control Abstract: We introduce a simple framework for learning aggressive maneuvers in flight control of UAVs. Having inspired from biological environment, dynamic movement primitives are analyzed and extended using nonlinear contraction theory. Accordingly, primitives of an ... |
Title: Mining Generalized Graph Patterns based on User Examples Abstract: There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecular structures are mined to discover fragments useful as features i... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.