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Exponentielles wachstum berechnen online dating


A system suffering from algorithmic bias results in systematic unfair treatment of certain users or data, with technical algorithmic bias arising specifically from technical constraints. We illustrate this problem, which so far has been neglected in high-dimensional data mining, for a real world music recommendation system. Due to a problem of measuring distances in high dimensional spaces, songs closer to the Exponentielles wachstum berechnen online dating of all data are recommended over and over again, while songs far from the center are not recommended at all.

We show that these so-called hub songs do not carry a specific semantic meaning and that deleting them from the data base promotes other songs to hub songs being recommended disturbingly often as a consequence.

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We argue that it is the ethical responsibility of data mining researchers to care about the fairness of their algorithms in high-dimensional spaces. Hubness, Algorithmic bias, Music recommendation, Citation: We illustrate this so far neglected problem for a real world music recommender, where due to a problem of measuring distances in high dimensional spaces, songs closer to the center of all data are recommended over and over again, while songs far from the center are not recommended at all.

We argue for the ethical responsibility of MIR researchers to assure that their algorithms are unbiased and fair. Algorithmic bias, Music recommendation, Ethical aspects Citation: Hubness reduction counters one particular aspect of the dimensionality curse, but suffers from quadratic algorithmic complexity.

We present approximate hubness reduction methods with linear complexity in time and space, thus enabling hubness reduction for large data for the first time. Furthermore, we introduce a new hubness measure especially suited for large data, which is, in Exponentielles wachstum berechnen online dating, readily interpretable. Experiments on synthetic and real-world data show that the approximations come at virtually no cost in accuracy in comparison with full hubness reduction.

Finally, we demonstrate improved transport mode detection in massive mobility data collected with Exponentielles wachstum berechnen online dating devices as concrete research application.

All methods are made publicly available in a free open source software package. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods.

We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial Exponentielles wachstum berechnen online dating. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.

Data and experiment code related to this paper are available for download at https: Professional forum moderators have annotated 11, posts according to seven categories they considered crucial for the efficient moderation of online discussions in the context of news articles. In addition to this taxonomy and annotated posts, the data set contains one million unlabeled posts. Our experimental results using six methods establish a first baseline for Exponentielles wachstum berechnen online dating these categories.

The data and our code are available for research purposes from https: We explore outlier detection in the presence of hubs and anti-hubs, i. We compare a classic distance based method to two new approaches, which have been designed to counter the negative effects of hubness, on six high-dimensional data sets.

We show that mainly anti-hubs pose a problem for outlier detection and that this can be improved by using a hubness-aware approach based on re-scaling the distance space.

Outlier detection, Hubness, Curse of dimensionality, Evaluation Citation: We compare a classic distance based method to two new approaches, which have been designed to counter the negative effects of hubness, on two standard music genre data sets.

We demonstrate that anti-hubs are responsible for many detection errors and that this can be improved by using a hubness-aware approach. Our multi-lingual system uses word lexica, a specialized tokenizer and rule-based shallow syntactic analysis to compute relevant features, and then trains statistical models support vector machines, random forests, etc.

The classification scores we achieve are very satisfactory on question detection and promising on to-do detection, on English and German data collections. NLP for user-generated content, text categorization, analysis and generation of conversations Citation: Theoretical Background and First Experiments. We apply this new approach in a music recommendation system based on an incrementally constructed knn graph.

We show that mutual proximity graphs yield much better connected graphs with better reachability compared to knn graphs and mutual knn graphs. Music recommendation, hubness, k-nearest neighbor graphs, mutual proximity Citation: Hubness is an aspect of the curse of dimensionality affecting many machine learning tasks.

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We present the Exponentielles wachstum berechnen online dating large scale empirical study to compare two competing hubness reduction techniques: We show that scaling consistently reduces hubness and improves nearest neighbor classification, while centering shows rather mixed results. Support vector classification is mostly unaffected by centering-based hubness reduction. Music, Dance, Drama, Films, Games, etc.

Robert Trappl invited presentation Keywords: The Nexus of Science, Technology and Arts: Cross-media serious game, Aquamorra, Childhood obesity, Design criteria Citation: Technologies for a digital world: Improving health across the lifespan.

A common approach is to combine standard game mechanics with linear content expected to deliver the intended message.

In Exponentielles wachstum berechnen online dating, we advocate an approach centered on player decisions and subjective experience, referring to innovative examples of learning by experience, both analog and digital. We present the design decisions underlying Aquamorra, a serious game to support the treatment of childhood obesity in the light of this approach.

Serious game, Obesity therapy, Game mechanics Citation: In their current form, these models are deterministic, and thus, given a trained model, there is only one possible performance that can be generated for a given piece.

By using a Bayesian framework, it is possible to produce a probabilistic interpretation of the models, and then generate performances by sampling from their predictive distributions. In this report we provide detailed derivations of the predictive distributions both the linear and non-linear versions of the BM approach Citation: Transformational creativity is a form of creativity where the creative behavior is induced by a transformation of the actor's conceptual Exponentielles wachstum berechnen online dating, that is, the representational system with which the actor interprets its environment.

In this report, we focus on ways of adapting systems of learned representations as they switch to performing one task to performing another. We describe an experimental comparison of multiple strategies for adaptation of learned features, and evaluate how effectively each of these strategies realizes the adaptation, in terms of the amount of training, and in terms of their ability to cope with restricted availability of training data.

We show, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch. They are often based on lower dimensional projections of high dimensional music similarity spaces. Such similarity spaces have already been shown to be negatively impacted by so-called hubs and anti-hubs.

These are points that appear very close or very far to many other data points due to a problem of measuring distances in high-dimensional spaces. We present an empirical study on how this phenomenon impacts three popular approaches to compute two-dimensional visualizations of music databases. We also show how the negative impact of hubs and anti-hubs can be reduced by re-scaling the high dimensional spaces before low dimensional projection. Music information retrieval, Hubness, Visualization, Dimensionality reduction, High-dimensional data analysis Exponentielles wachstum berechnen online dating Improving visualization of high-dimensional music similarity spaces, 16th International Society for Music Information Retrieval Conference, Malaga, Spain, Due to a problem of measuring distances in high dimensions, hub objects are recommended over and over again while anti-hubs are nonexistent in recommendation lists.

After reviewing the theory concerning the hubness phenomenon, we present methods which are able to decisively diminish hubness and its adverse effects in music and general multimedia datasets. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points.

In an empirical study of K-medoids clustering we show that hubness gives rise to very unbalanced cluster sizes resulting in impaired internal and external evaluation indices.

We compare three methods which reduce Exponentielles wachstum berechnen online dating in the distance spaces and show that with the balancing of the clusters evaluation indices improve.

This is done using artificial and real data sets from diverse domains. Clustering, Hubness, Curse of dimensionality Citation: Schnitzer, Dominik and Flexer, Arthur. The conditional distributions of visible and hidden units, and the log likelihood gradient with respect to the model parameters.

It is not meant as a general introduction to RBMs, but as a supplement helping to follow the mathematics. Restricted Boltzmann Machines Citation: Restricted Boltzmann Machine Derivations. Exponentielles wachstum berechnen online dating model is an extension of prior work by Grachten and Widmer [Grachten and Widmer, ] and is a generalization of the work by Grachten et al.

By assuming the prior distribution of the model parameters to be Gaussian with arbitrary mean and covariance, this model allows for specifying musical knowledge, and for modeling multiple distinct performances of the same piece. We show that in its current state, the model performs at least on a par with the original approach. Bayesian linear basis models with gaussian priors for musical expression. The configuration of most current tools for controlling agents is however implementation-specific and not tailored to the needs of authors.

Based on literature review; a questionnaire evaluation of authors' preferences for character creation; and a case study of an author's conceptualization Exponentielles wachstum berechnen online dating this process, we investigate the different methods of configuration available in current agent architectures, reviewing discrepancies and matches.

Given these relations, promising approaches to configuration are identified, based on: SMS can be viewed from two theoretical perspectives: According to the dynamical systems theory, SMS involves non-linear phase and period adjustments to a set of coupled internal oscillators. The information-processing approach posits linear phase and period correction of the internal timekeeper.

Models derived from these theories are commonly tested in non-musical tapping experiments.

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