Browsing by Subject "supervised learning"
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Item Open Access Batch learning of disjoint feature intervals(Bilkent University, 1996) Akkuş, AynurThis 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.Item Open Access Classification with overlapping feature intervals(Bilkent University, 1995) Koç, Hakime ÜnsalThis 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.Item Open Access Fire detection algorithms using multimodal signal and image analysis(Bilkent University, 2009) Töreyin, Behçet UğurDynamic textures are common in natural scenes. Examples of dynamic textures in video include fire, smoke, clouds, volatile organic compound (VOC) plumes in infra-red (IR) videos, trees in the wind, sea and ocean waves, etc. Researchers extensively studied 2-D textures and related problems in the fields of image processing and computer vision. On the other hand, there is very little research on dynamic texture detection in video. In this dissertation, signal and image processing methods developed for detection of a specific set of dynamic textures are presented. Signal and image processing methods are developed for the detection of flames and smoke in open and large spaces with a range of up to 30m to the camera in visible-range (IR) video. Smoke is semi-transparent at the early stages of fire. Edges present in image frames with smoke start loosing their sharpness and this leads to an energy decrease in the high-band frequency content of the image. Local extrema in the wavelet domain correspond to the edges in an image. The decrease in the energy content of these edges is an important indicator of smoke in the viewing range of the camera. Image regions containing flames appear as fire-colored (bright) moving regions in (IR) video. In addition to motion and color (brightness) clues, the flame flicker process is also detected by using a Hidden Markov Model (HMM) describing the temporal behavior. Image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain. High frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. Temporal and spatial clues extracted from the video are combined to reach a final decision.Signal processing techniques for the detection of flames with pyroelectric (passive) infrared (PIR) sensors are also developed. The flame flicker process of an uncontrolled fire and ordinary activity of human beings and other objects are modeled using a set of Markov models, which are trained using the wavelet transform of the PIR sensor signal. Whenever there is an activity within the viewing range of the PIR sensor, the sensor signal is analyzed in the wavelet domain and the wavelet signals are fed to a set of Markov models. A fire or no fire decision is made according to the Markov model producing the highest probability. Smoke at far distances (> 100m to the camera) exhibits different temporal and spatial characteristics than nearby smoke and fire. This demands specific methods explicitly developed for smoke detection at far distances rather than using nearby smoke detection methods. An algorithm for vision-based detection of smoke due to wild fires is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) smoke-colored regions, (iii) rising regions, and (iv) shadows. Each sub-algorithm yields its own decision as a zero-mean real number, representing the confidence level of that particular subalgorithm. Confidence values are linearly combined for the final decision. Another contribution of this thesis is the proposal of a framework for active fusion of sub-algorithm decisions. Most computer vision based detection algorithms consist of several sub-algorithms whose individual decisions are integrated to reach a final decision. The proposed adaptive fusion method is based on the least-mean-square (LMS) algorithm. The weights corresponding to individual sub-algorithms are updated on-line using the adaptive method in the training (learning) stage. The error function of the adaptive training process is defined as the difference between the weighted sum of decision values and the decision of an oracle who may be the user of the detector. The proposed decision fusion method is used in wildfire detection.