Online change detection in exponential families with unknown parameters

International audienceThis paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common statistical models and corresponding distance functions. Using results from convex duality, we also derive an efficient scheme to compute the exact statistics sequentially, which allows their use in online settings where they are usually approximated for the sake of tractability. This is applied to real-world datasets of various natures, including onset detection in audio signals

The Impact of Profit Taxation on Capitalized Investment with Options to Delay and Divest

In entrepreneurial decisions making uncertain future profits often are a main characteristics of real investment opportunities. If investors can react to uncertainty the degree of irreversibility and timing flexibility inherent in the available project should be integrated into the decision calculus. In this paper we investigate the interdependencies of effects from profit taxation and real options. We model an investment decision including an option to invest and an option to abandon. We show that increasing the tax rate can lead to paradoxical tax effects, i.e. may foster an investor's willingness to invest into a capitalized investment. Instead, if we abstract from the possibility to abandon the investment object such paradoxical effect cannot be identified. Determining the after-tax value of the option to enter the investment project with and without an abandonment option we receive a critical cash flow cutoff level. We find that the value of the option to abandon depends on the tax rate and the amount of periodical cash flows. The option value can be increasing or decreasing in the tax rate. We find scenarios with paradoxical tax effects and show that the observed paradoxical effects are due to the presence of the real abandonment option itself. This finding contributes to the stream of literature that explains potential sources of paradoxical tax effects. The generated decision rules are helpful for investors facing risky investment opportunities and for discussing the economic impact of tax reforms. Furthermore, we highlight the overwhelming importance of integrating taxes in typically applied valuation approaches

Portfolio Saham Saham Perbankan Dengan Analytical Hierarchy Process Di Bursa Efek Surabaya

We benchmark several SVM objective functions for large-scale image classification. We consider one-vs-rest, multi-class, ranking, and weighted approximate ranking SVMs. A comparison of online and batch methods for optimizing the objectives shows that online methods perform as well as batch methods in terms of classification accuracy, but with a significant gain in training speed. Using stochastic gradient descent, we can scale the training to millions of images and thousands of classes. Our experimental evaluation shows that ranking-based algorithms do not outperform the one-vs-rest strategy when a large number of training examples are used. Furthermore, the gap in accuracy between the different algorithms shrinks as the dimension of the features increases. We also show that learning through cross-validation the optimal rebalancing of positive and negative examples can result in a significant improvement for the one-vs-rest strategy. Finally, early stopping can be used as an effective regularization strategy when training with online algorithms. Following these “good practices”, we were able to improve the state-of-the-art on a large subset of 10K classes and 9M images of ImageNet from 16.7 % Top-1 accuracy to 19.1%

Attribute-based Classification with Label-embedding

International audienceAttributes are an intermediate representation whose purpose is to enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct class has a higher compatibility than the incorrect ones. Experimental results on two standard image classification datasets are presented, resp. on the Animals With Attributes and on Caltech-UCSD-Birds datasets

A Comparison of Statistical Learning Approaches for Engine Torque Estimation

Engine torque estimation has important applications in the automotive industry: for example, automatically setting gears, optimizing engine performance, reducing emissions and designing drivelines. A methodology is described for the on-line calculation of torque values from the gear, the accelerator pedal position and the engine rotational speed. It is based on the availability of input-torque experimental signals that are preprocessed (resampled, filtered and segmented) and then learned by a statistical machine-learning method. Four methods, spanning the main learning principles, are reviewed in a unified framework and compared using the torque estimation problem: linear least squares, linear and non-linear neural networks and support vector machines. It is found that a non-linear model structure is necessary for accurate torque estimation. The most efficient torque model built is a non-linear neural network that achieves about 2 % test normalized mean square error in nominal conditions

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