We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for ...

The processing performed by a feed-forward neural network is oft en int erpreted through use of decision hyperplanes at each layer. T he adaptation process, however, is normally explained using the picture of gradient descent of an error land scape. In thi s paper t he dynamics of t he decision hyperplanes is used as t he model of the adaptat ion process. An electro-mechanical analogy is drawn ...

Decision table describing n objects in terms of k classification attributes and one decision attribute can be seen as a collection of n points in k-dimensional space. Each point is classified either as positive or negative. The goal of this paper is to present an efficient strategy for constructing possibly the smallest number of hyperplanes so each area surrounded by them contains a group of p...

In this technical report a novel method is proposed that extends the decision tree framework, allowing standard decision tree classifiers to provide a unique certainty value for every input sample they classify. This value is calculated for every input sample individually and represents the classifier's certainty in the classification. The algorithm proposed in this report is not limited to axi...

|A signal-space detector estimates the channel input symbol based on the location of the nite-length observation signal in a multi-dimensional signal-space. The decision boundary is formed by a set of hyperplanes. The resulting detector structure consists of linear discriminant functions, threshold detectors, and a Boolean logic function. Our goal is to minimize the number of linear discriminan...

We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an -class problem, the DDAG contains classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on and on the margin ...

In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our ...

We present a signal space partitioning technique for realizing the optimal Bayesian decision feedback equalizer (DFE). It is known that when the signal-to-noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. The proposed technique determines these hyperplanes explicitly and uses them to partition t...

Perceptron Decision Trees (also known as Linear Machine DTs, etc.) are analysed in order that data-dependent Structural Risk Minimization can be applied. Data-dependent analysis is performed which indicates that choosing the maximal margin hyperplanes at the decision nodes will improve the generalization. The analysis uses a novel technique to bound the generalization error in terms of the marg...