Browsing by Subject "Machine learning."

Now showing 1 - 6 of 6

Open Access
Batch learning of disjoint feature intervals
(1996) Akkuş, Aynur
This thesis presents several learning algorithms for multi-concept descriptions in the form of disjoint feature intervals, called Feature Interval Learning algorithms (FIL). These algorithms are batch supervised inductive learning algorithms, and use feature projections of the training instances for the representcition of the classification knowledge induced. These projections can be generalized into disjoint feature intervals. Therefore, the concept description learned is a set of disjoint intervals separately for each feature. The classification of an unseen instance is based on the weighted majority voting among the local predictions of features. In order to handle noisy instances, several extensions are developed by placing weights to intervals rather than features. Empirical evaluation of the FIL algorithms is presented and compared with some other similar classification algorithms. Although the FIL algorithms achieve comparable accuracies with other algorithms, their average running times are much more less than the others. This thesis also presents a new adaptation of the well-known /s-NN classification algorithm to the feature projections approach, called A:-NNFP for k-Nearest Neighbor on Feature Projections, based on a majority voting on individual classifications made by the projections of the training set on each feature and compares with the /:-NN algorithm on some real-world and cirtificial datasets.
Open Access
Benefit maximizing classification using feature intervals
(2002) İkizler, Nazlı
For a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this assumption is not convenient. For instance, in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more serious than its opposite. For this reason, there is a great need for new classification methods that can handle asymmetric cost and benefit constraints of classifications. In this thesis, we discuss cost-sensitive classification concepts and propose a new classification algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the feature projection based knowledge representation. In the framework of BMFI, we introduce five different voting methods that are shown to be effective over different domains. A number of generalization and pruning methodologies based on benefits of classification are implemented and experimented. Empirical evaluation of the methods has shown that BMFI exhibits promising performance results compared to recent wrapper cost-sensitive algorithms, despite the fact that classifier performance is highly dependent on the benefit constraints and class distributions in the domain. In order to evaluate costsensitive classification techniques, we describe a new metric, namely benefit accuracy which computes the relative accuracy of the total benefit obtained with respect to the maximum possible benefit achievable in the domain.
Open Access
Classification with overlapping feature intervals
(1995) Koç, Hakime Ünsal
This thesis presents a new form of exemplar-based learning method, based on overlapping feature intervals. Classification with Overlapping Feature Intervals (COFI) is the particular implementation of this technique. In this incremental, inductive and supervised learning method, the basic unit of the representation is an interval. The COFI algorithm learns the projections of the intervals in each class dimension for each feature. An interval is initially a point on a class dimension, then it can be expanded through generalization. No specialization of intervals is done on class dimensions by this algorithm. Classification in the COFI algorithm is based on a majority voting among the local predictions that are made individually by each feature.
Open Access
Learning with feature partitions
(1993) Şirin, İzzet
This thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques.
Open Access
Motion planning of a mechanical snake using neural networks
(1998) Fidan, Barış
In this thesis, an optimal strategy is developed to get a mechanical snake (a robot composed of a sequence of articulated links), which is located arbitrarily in an enclosed region, out of the region through a specified exit without violating certain constraints. This task is done in two stages: Finding an optimal path that can be tracked, and tracking the optimal path found. Each stage is implemented by a neural network. Neural network of the second stage is constructed by direct evaluation of the weights after designing an efficient structure. Two independent neural networks are designed to implement the first stage, one trained to implement an algorithm we have derived to generate minimal paths and the other trained using multi-stage neural network approach. For the second design, the intuitive multi-stage neural network back propagation approach in the literature is formalized.
Open Access
Using multiple sources of information for constraint-based morphological disambiguation
(1996) Tür, Gökhan
This thesis presents a constraint-based morphological disambiguation approach that is applicable to languages with complex morphology-specifically agglutiriiitive languages with productive inflectional and derivational morphological phenomena. For morphologicciJly comiDlex languages like Turkish, automatic morphological disarnbigucition involves selecting for each token rnorphologiccil parse(s), with the right set of inflectional and derivational markers. Our system combines corpus independent hand-crafted constraint rules, constraint rules that are lecirned via unsupervised learning from a training corpus, and additioiml stcitistiCcil information obtcvined from the corpus to be morphologically disarnbigucited. The hcind-crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall. In certain respects, our ai^proach has been motivated by Brill’s recent work [6], but with the observation that his transformational approach is not directly applicable to languages like Turkish. Our approach also uses a novel approach to unknown word processing by employing a secondary morphological processor which recovers any relevant inflectional and derivational information from a lexical item whose root is unknown. With this approach, well below 1% of the tokens remains as unknown in the texts we have experimented with. Our results indicate that by combining these hand-crafted, statistical and learned information sources, we can attain a reccill of 96 to 97% with a corresponding precision of 93 to 94%, and ambiguity of 1.02 to 1.03 parses per token.