Search results for: J. Smola

Items from 1 to 20 out of 28 results

article

Język, nie gwara

J. Smola

Łowiec Polski > 2021 > 6

article

CTX‐M‐producing Escherichia coli in pigs from a Czech farm during production cycle

M. Zelendova, M. Dolejska, M. Masarikova, I. Jamborova, more

Letters in Applied Microbiology > 71 > 4 > 369 - 376

We evaluated the prevalence and epidemiology of extended‐spectrum beta‐lactamase (ESBL)‐producing Escherichia coli isolates in pigs during production cycle on a Czech farm with the history of previous use of ceftiofur. ESBL‐producing E. coli isolates were obtained from rectal swabs from pigs of different age groups (suckling piglets, weaned piglets, growers and sows). Collected samples were directly...

article

Relikwie świętego Huberta w Polsce

J. Mucha, J. Smola

Łowiec Polski > 2019 > 12

chapter

Sampling Matters in Deep Embedding Learning

R. Manmatha, Chao-Yuan Wu, Alexander J. Smola, Philipp Krahenbuhl

2017 IEEE International Conference on Computer Vision (ICCV) > 2859 - 2867

2017 IEEE International Conference on Computer Vision (ICCV)

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that...

chapter

Predicting time series with support vector machines

K. -R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, more

Lecture Notes in Computer Science > Artificial Neural Networks — ICANN'97 > 999-1004

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform noise)...

chapter

Regularized Principal Manifolds

Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf

Lecture Notes in Computer Science > Computational Learning Theory > Statistical Theory of Learning and Pattern Recognition > 214-229

Many settings of unsupervised learning can be viewed as quantization problems — the minimization of the expected quantization error subject to some restrictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised settings. Moreover, this setting is very closely related to both principal curves and the generative topographic map. ...

chapter

Bayesian Kernel Methods

Alexander J. Smola, Bernhard Schölkopf

Lecture Notes in Computer Science > Advanced Lectures on Machine Learning > 65-117

Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.

chapter

Transductive Gaussian Process Regression with Automatic Model Selection

Quoc V. Le, Alex J. Smola, Thomas Gärtner, Yasemin Altun

Lecture Notes in Computer Science > Machine Learning: ECML 2006 > Long Papers > 306-317

In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at training time. Transductive inference tries to improve the predictive accuracy of learning algorithms by making use of the information contained in these test instances. Although this description of transductive inference applies...

chapter

A Short Introduction to Learning with Kernels

Bernhard Schölkopf, Alexander J. Smola

Lecture Notes in Computer Science > Advanced Lectures on Machine Learning > 41-64

We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over applications of kernel methods.

chapter

Universal Clustering with Regularization in Probabilistic Space

Vladimir Nikulin, Alex J. Smola

Lecture Notes in Computer Science > Machine Learning and Data Mining in Pattern Recognition > Clustering: Basics > 142-152

We propose universal clustering in line with the concepts of universal estimation. In order to illustrate above model we introduce family of power loss functions in probabilistic space which is marginally linked to the Kullback-Leibler divergence. Above model proved to be effective in application to the synthetic data. Also, we consider large web-traffic dataset. The aim of the experiment is to explain...

chapter

A Generalized Representer Theorem

Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola

Lecture Notes in Computer Science > Computational Learning Theory > 416-426

Wahba’s classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The...

chapter

Newton-Like Methods for Nonparametric Independent Component Analysis

Hao Shen, Knut Hüper, Alexander J. Smola

Lecture Notes in Computer Science > Neural Information Processing > Independent Component Analysis and Blind Source Separation > 1068-1077

The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable manifold...

chapter

Entropy Numbers, Operators and Support Vector Kernels

Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf

Lecture Notes in Computer Science > Computational Learning Theory > Statistical Theory of Learning and Pattern Recognition > 285-299

We derive new bounds for the generalization error of feature space machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs are based on a viewpoint that is apparently novel in the field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinite dimensional...

book

Advanced Lectures on Machine Learning

Shahar Mendelson, Alexander J. Smola

Lecture Notes in Computer Science

Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics...

article

Solution Structure of 4′-Phosphopantetheine - GmACP3 from Geobacter metallireducens : A Specialized Acyl Carrier Protein with Atypical Structural Features and a Putative Role in Lipopolysaccharide Biosynthesis

Theresa A. Ramelot, Matthew J. Smola, Hsiau-Wei Lee, Colleen Ciccosanti, more

Biochemistry > 2011 > 50 > 9 > 1442 - 1453

chapter

Distributed Flow Algorithms for Scalable Similarity Visualization

N Quadrianto, D Schuurmans, A J Smola

2010 IEEE International Conference on Data Mining Workshops > 1220 - 1227

2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010)

We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps...

article

Kernelized Sorting

N Quadrianto, A J Smola, Le Song, T Tuytelaars

IEEE Transactions on Pattern Analysis and Machine Intelligence > 2010 > 32 > 10 > 1809 - 1821

Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we develop an approach which is able to perform matching by requiring a similarity measure only within each of the classes. This is achieved by maximizing the dependency between matched pairs of observations by means of the...

chapter

Learning Graph Matching

Tiberio S. Caetano, Li Cheng,, Quoc V. Le, Alex J. Smola

2007 IEEE 11th International Conference on Computer Vision > 1 - 8

2007 11th IEEE International Conference on Computer Vision

As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment...

article

The levels of PCV2 specific antibodies and viremia in pigs

P. Carasova, V. Celer, K. Takacova, M. Trundova, more

Research in Veterinary Science > 2007 > 83 > 2 > 274-278

Porcine circovirus 2 quantification by real-time PCR and determination of virus specific antibodies using a peptide based ELISA test was performed on a total of 400 serum samples.Samples for antibody measurement and virus quantification were obtained from a conventional pig farm with a clinical form of post-weaning multisystemic wasting syndrome. Samples were taken during the post-weaning period....

article

Isolation of Lawsonia intracellularis specific single-chain Fv antibody fragments from phage display library

K. Dezorzová-Tomanová, D. Molinková, M. Pekarová, V. Celer, more

Research in Veterinary Science > 2007 > 83 > 1 > 85-90

Single-chain antibodies (scFv) exhibiting specific binding to Lawsonia intracellularis were isolated from a phagemid library expressing scFvs molecules on the surface of filamentous bacteriophages. For scFv selection whole bacterial cells were used and individual clones were tested in ELISA test. The total of seven unique clones with different fingerprint profiles was isolated. All clones were able...

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Keywords

DATA MINING (3)
SUPPORT VECTOR MACHINES (3)
CONVERGENCE (2)
JOINTS (2)
KERNEL METHODS (2)
KERNELS (2)
MACHINE LEARNING (2)
OPTIMIZATION (2)
REGRESSION (2)
ANTIBIOTIC RESISTANCE (1)
ANTIBODIES (1)
ARMA MODELS AND DYNAMICAL SYSTEMS (1)
BINET-CAUCHY THEOREM (1)
BIPARTITE GRAPH (1)
CEFTIOFUR (1)
CIRCOVIRUS (1)
CLASSIFICATION (1)
COLOR SIMILARITY (1)
COMPUTATIONAL MODELING (1)
COMPUTER VISION (1)
CONVEX-CONCAVE SADDLE POINT PROBLEM (1)
CR SUBJECT CLASSIFICATION:I.2.6 (1)
DATA ANALYSIS (1)
DIGIT RECOGNITION (1)
DISTRIBUTED ALGORITHMS (1)
DISTRIBUTED FLOW ALGORITHMS (1)
DYNAMIC SCENES (1)
DYNAMIC TEXTURES (1)
ESCHERICHIA COLI (1)
ESTIMATION (1)
EXTENDED‐SPECTRUM BETA‐LACTAMASE (1)
F.1.1 (1)
FACE (1)
FEATURE EXTRACTION (1)
FIRST ORDER APPROXIMATION METHOD (1)
FIRST ORDER METHOD (1)
FLOW NETWORKS (1)
G.3 (1)
GAUSSIAN KERNEL (1)
H.2.8 (1)
HILBERT SPACES (1)
HILBERT-SCHMIDT INDEPENDENCE CRITERION (1)
HILBERT-SCHMIDT INDEPENDENCE CRITERION. (1)
I.4.7 (1)
I.4.8 (1)
I.5 (1)
I.7.5 (1)
IMAGE LAYOUTING (1)
IMAGE RETRIEVAL (1)
IMMUNOFLUORESCENCE (1)
INVERSE PROBLEMS (1)
JEZYK LOWIECKI (1)
KERNEL (1)
KERNELIZED SORTING (1)
KERNMETHODEN (1)
KEY WORDS. KERNELS, SUPPORT VECTOR MACHINES, REGULARIZATION, INVERSE PROBLEMS, REGRESSION, PATTERN RECOGNITION. (1)
KLASSIFIKATION (1)
KULT SWIETEGO HUBERTA (1)
LAWSONIA INTRACELLULARIS (1)
LINEAR PROGRAMMING (1)
LOWIECTWO (1)
MACHINE LEARNING ALGORITHMS (1)
MATCHING (1)
MERKMALSEXTRAKTION (1)
MODEL SELECTION (1)
MOSAIC GENERATION (1)
MUSTERERKENNUNG (1)
NEURAL NETWORKS (1)
NEURONALE NETZE (1)
NOISE MEASUREMENT (1)
OBJECT ALIGNMENT (1)
OBJECT MATCHING (1)
OPTIMAL Ν (1)
PARALLEL PROCESSING (1)
PATRON (1)
PATTERN MATCHING (1)
PATTERN RECOGNITION (1)
PATTERN RECOGNITION. (1)
PIGS (1)
PLASMIDS (1)
POPULARYZACJA (1)
PROBABILITY DENSITY FUNCTION (1)
QUADRATIC ASSIGNMENT PROBLEM (1)
QUASI-NEWTON STEPS (1)
REAL-TIME (1)
REGRESSION ESTIMATION (1)
REGULARIZATION (1)
RELIKWIE SW.HUBERTA (1)
REPRODUCING KERNEL HILBERT SPACES (1)
RISK MINIMIZATION (1)
SCALABLE SIMILARITY VISUALIZATION (1)
SCFV (1)
SCHLSSELWRTER: MASCHINELLES LERNEN (1)
SIMILARITY MEASURE (1)
SLOWNIKI SPECJALISTYCZNE (1)
SORTING (1)
SPECIFICITY (1)
SUPPORT VECTOR MACHINE PARAMETERS (1)
SUPPORT VECTOR REGRESSION (1)
SWIETY HUBERT (1)
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Springer (15)
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ieee (4)
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ACS (1)
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Algorithmica (2)
Neural Networks (2)
Research in Veterinary Science (2)
Łowiec Polski (2)
Annals of Animal Science. Supplement (1)
Biochemistry (1)
Computer Science - Research and Development (1)
IEEE Transactions on Pattern Analysis and Machine Intelligence (1)
International Journal of Computer Vision (1)
Letters in Applied Microbiology (1)
Statistics and Computing (1)
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INFONA - science communication portal

Search results for: J. Smola

Język, nie gwara

CTX‐M‐producing Escherichia coli in pigs from a Czech farm during production cycle

Relikwie świętego Huberta w Polsce

Sampling Matters in Deep Embedding Learning

Predicting time series with support vector machines

Regularized Principal Manifolds

Bayesian Kernel Methods

Transductive Gaussian Process Regression with Automatic Model Selection

A Short Introduction to Learning with Kernels

Universal Clustering with Regularization in Probabilistic Space

A Generalized Representer Theorem

Newton-Like Methods for Nonparametric Independent Component Analysis

Entropy Numbers, Operators and Support Vector Kernels

Advanced Lectures on Machine Learning

Solution Structure of 4′-Phosphopantetheine - GmACP3 from Geobacter metallireducens : A Specialized Acyl Carrier Protein with Atypical Structural Features and a Putative Role in Lipopolysaccharide Biosynthesis

Distributed Flow Algorithms for Scalable Similarity Visualization

Kernelized Sorting

Learning Graph Matching

The levels of PCV2 specific antibodies and viremia in pigs

Isolation of Lawsonia intracellularis specific single-chain Fv antibody fragments from phage display library

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