Catálogo de publicaciones - libros

We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current label as a weight during training. The probability vector should be calculated such that clean instances have a high confidence on its current label, while mislabeled instances have a low confidence on its current label and a high confidence on its correct label. Past research focuses on techniques that either discard or correct instances. This paper proposes that discarding and correcting are special cases of instance weighting, and thus, part of this framework. We propose a method that uses clustering to calculate a probability distribution over the class labels for each instance. We demonstrate that our method improves classifier accuracy over the original training set. We also demonstrate that instance weighting can outperform discarding.

Choosing a suitable feature representation for structured data is a non-trivial task due to the vast number of potential candidates. Ideally, one would like to pick a small, but informative set of structural features, each providing complementary information about the instances. We frame the search for a suitable feature set as a combinatorial optimization problem. For this purpose, we define a scoring function that favors features that are as dissimilar as possible to all other features. The score is used in a stochastic local search (SLS) procedure to maximize the diversity of a feature set. In experiments on small molecule data, we investigate the effectiveness of a forward selection approach with two different linear classification schemes.

In this paper, we propose a new and general preprocessor algorithm, called , which converts any cost-insensitive classification algorithms into cost-sensitive ones. is based on cost proportional roulette sampling technique (called in short). is closely related to Costing, another cost-sensitive meta-learning algorithm, which is based on rejection sampling. Unlike rejection sampling which produces smaller samples, can generate different size samples. To further improve its performance, we apply ensemble (bagging) on ; the resulting algorithm is called . Our experiments show that outperforms Costing and other meta-learning methods in most datasets tested. In addition, we investigate the effect of various sample sizes and conclude that reduced sample sizes (as in rejection sampling) cannot be compensated by increasing the number of bagging iterations.

Most traffic management and optimization tasks, such as accident detection or optimal vehicle routing, require an ability to adequately model, reason about and predict irregular and stochastic behavior. Our goal is to create a probabilistic model of traffic flows on highway networks that is realistic from the point of applications and at the same time supports efficient learning and inference. We study several multivariate probabilistic models and analyze their respective strengths. To balance accuracy and efficiency, we propose a novel learning model, mixture of Gaussian trees, and show its advantages in learning and inference. All models are evaluated on real-world traffic flow data from highways of the Pittsburgh area.

We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed and the task is to uncover the hidden components using the observation only. We carry out this task for the undercomplete case (uBSSD): we reduce the original uBSSD task via linear prediction to independent subspace analysis (ISA), which we can solve. As it has been shown recently, applying temporal concatenation can also reduce uBSSD to ISA, but the associated ISA problem can easily become ‘high dimensional’ [1]. The new reduction method circumvents this dimensionality problem. We perform detailed studies on the efficiency of the proposed technique by means of numerical simulations. We have found several advantages: our method can achieve high quality estimations for smaller number of samples and it can cope with deeper temporal convolutions.

We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step’s state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog.

Imitation-based learning is a general mechanism for rapid acquisition of new behaviors in autonomous agents and robots. In this paper, we propose a new approach to learning by imitation based on parameter learning in probabilistic graphical models. Graphical models are used not only to model an agent’s own dynamics but also the dynamics of an observed teacher. Parameter tying between the agent-teacher models ensures consistency and facilitates learning. Given only observations of the teacher’s states, we use the expectation-maximization (EM) algorithm to learn both dynamics and policies within graphical models. We present results demonstrating that EM-based imitation learning outperforms pure exploration-based learning on a benchmark problem (the FlagWorld domain). We additionally show that the graphical model representation can be leveraged to incorporate domain knowledge (e.g., state space factoring) to achieve significant speed-up in learning.

The paper investigates modification of backpropagation algorithm, consisting of discretization of neural network weights after each training cycle. This modification, aimed at overfitting reduction, restricts the set of possible values of weights to a discrete subset of real numbers, leading to much better generalization abilities of the network. This, in turn, leads to higher accuracy and a decrease in error rate by over 50% in extreme cases (when overfitting is high).

Discretization is performed nondeterministically, so as to keep expected value of discretized weight equal to original value. In this way, global behavior of original algorithm is preserved. The presented method of discretization is general and may be applied to other machine-learning algorithms. It is also an example of how an algorithm for continuous optimization can be successfully applied to optimization over discrete spaces. The method was evaluated experimentally in WEKA environment using two real-world data sets from UCI repository.

We study the problem of , which aims at “aligning” different data sets that share a similar intrinsic manifold provided some supervision. Unlike traditional methods that rely on pairwise correspondences between the two data sets, our method only needs some relative comparison information like “A is more similar to B than A is to C”. This method provides a more flexible way to acquire the prior knowledge for alignment, thus is able to handle situations where corresponding pairs are hard or impossible to identify. We optimize our objective based on the graphs that give discrete approximations of the manifold. Further, the problem is formulated as a (SDP) problem which can readily be solved. Finally, experimental results are presented to show the effectiveness of our method.

Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is an open issue that how to compute the robust discriminant transformation. In this paper, we first show that supervised graph embedding algorithms share a general criterion (Generalized Rayleigh Quotient). Through novel perspective to Generalized Rayleigh Quotient, we propose a general solution, called , for extracting the robust discriminant transformation of Supervised Graph Embedding. Finally, extensive experiments on real-world data are performed to demonstrate the effectiveness and robustness of our proposed GSSGE.