Bibliografie tematiche / Soft classification

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Letteratura scientifica selezionata sul tema "Soft classification"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 6 febbraio 2022

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Articoli di riviste sul tema "Soft classification"

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Villandré, Luc, Benjamin Rich e Antonio Ciampi. "Soft Classification Trees". Communications in Statistics - Theory and Methods 41, n. 16-17 (agosto 2012): 3244–58. http://dx.doi.org/10.1080/03610926.2011.632103.

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Sambu Seo, M. Bode e K. Obermayer. "Soft nearest prototype classification". IEEE Transactions on Neural Networks 14, n. 2 (marzo 2003): 390–98. http://dx.doi.org/10.1109/tnn.2003.809407.

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Bonatz, Ekkehard, e Jorge E. Alonso. "Classification of Soft-Tissue Injuries". Techniques in Orthopaedics 10, n. 2 (1995): 73–78. http://dx.doi.org/10.1097/00013611-199501020-00003.

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Ibrahim, David A., Alan Swenson, Adam Sassoon e Navin D. Fernando. "Classifications In Brief: The Tscherne Classification of Soft Tissue Injury". Clinical Orthopaedics and Related Research® 475, n. 2 (14 luglio 2016): 560–64. http://dx.doi.org/10.1007/s11999-016-4980-3.

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Allison, Peter A. "Konservat-Lagerstätten:cause and classification". Paleobiology 14, n. 4 (1988): 331–44. http://dx.doi.org/10.1017/s0094837300012082.

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A review of the processes required for exceptional preservation of soft-bodied fossils demonstrates that anoxia does not significantly inhibit decay and emphasizes the importance of early diagenetic mineralization. Early diagenesis is the principal factor amongst the complex processes leading to soft-part preservation. The development of a particular preservational mineral is controlled by rate of burial, amount of organic detritus, and salinity. A new causative classification of soft-bodied fossil biotas is presented based upon fossil mineralogy and mineral paragenesis.
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Jaiswal, Tarun, Dr S. Jaiswal e Dr Ragini Shukla. "Soft Computing Techniques Based Image Classification using Support Vector Machine Performance". International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30 aprile 2019): 1645–50. http://dx.doi.org/10.31142/ijtsrd23437.

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Mandal, Sudip, Goutam Saha e Rajat K. Pal. "A Comparative Study on Disease Classification using Different Soft Computing Techniques". SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, n. 04 (8 agosto 2014): 22–29. http://dx.doi.org/10.9756/sijcsea/v2i4/0203110201.

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Mackay, Bruce. "Ultrastructural Classification of Soft Tissue Neoplasms". Ultrastructural Pathology 9, n. 3-4 (gennaio 1985): 179. http://dx.doi.org/10.3109/01913128509074571.

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Gómez, D., G. Biging e J. Montero. "Accuracy statistics for judging soft classification". International Journal of Remote Sensing 29, n. 3 (21 dicembre 2007): 693–709. http://dx.doi.org/10.1080/01431160701311325.

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Roverso, Davide. "Soft computing tools for transient classification". Information Sciences 127, n. 3-4 (agosto 2000): 137–56. http://dx.doi.org/10.1016/s0020-0255(00)00035-9.

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Tesi sul tema "Soft classification"

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Phillips, Rhonda D. "A Probabilistic Classification Algorithm With Soft Classification Output". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26701.

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This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The feature reduction method introduced in this thesis is based on the singular value decomposition (SVD). This feature reduction technique demonstrated that SVD-based feature reduction can lead to more accurate IGSCR classifications than PCA-based feature reduction. This thesis describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. The adaptive filtering scheme improves image quality as shown by estimated SNRs and classification accuracy improvements greater than 10%. The continuous iterative guided spectral class rejection (CIGSCR) classification method is based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. This type of semisupervised classification method is particularly useful in remote sensing where datasets are large, training data are difficult to acquire, and clustering makes the identification of subclasses adequate for training purposes less difficult. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations.
Ph. D.
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Stolpmann, Alexander. "An intelligent soft-computing texture classification system". Thesis, University of South Wales, 2005. https://pure.southwales.ac.uk/en/studentthesis/an-intelligent-softcomputing-texture-classification-system(a43eb831-a799-438b-9112-3ce1df432fe9).html.

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The aim of this research work was to obtain a system that classifies texture. This so called Texture Classification System is not a system for one special task or group of tasks. It is a general approach that shows a way towards real artificial vision. Finding ways to enable computerised systems to visually recognise its surroundings is of increasing importance for the industry and society at large. To reach this goal not only objects but less well describable texture has to be identified within an image. To achieve this aim a number of objectives had to be met. At first a review of how natural vision works was done to better understand the complexity of visual systems. This is followed by a more detailed definition of what texture is. Next a review of image processing techniques, of statistical methods and of soft-computing methods was made to identify those that can be used or improved for the Texture Classification System. A major objective was to create the structure of the Texture Classification System. The design presented in this work is the framework for a multitude of modules arranged in groups and layers with multiple feedback and optimisation possibilities. The main achievement is a system for texture classification for which natural vision was used as a " blue-print". A more detailed definition of what texture is was made and a new texture library was started. The close review of image processing techniques provided a variety of applicable methods, as did the review and enhancement of statistical methods. Some of those methods were improved or used in a new way. Neural networks and fuzzy clustering were applied for classification, while genetic algorithms provide a means for self optimisation. The concepts and methods have been used for a number of projects next to texture classification itself. This work presents applications for fault detection in glass container manufacturing, quality control of veneer, positioning control of steel blocks in a rotation oven, and measurement of hair gloss. With the Texture Classification System a new, holistic approach for complex image processing and artificial vision tasks is being contributed. It uses a modular combination of statistics, image processing and soft-computing methods, easily adaptable to new tasks, includes new ideas for high order statistics, and incorporates self optimisation to achieve lean sub-systems. The system allows multiple feedbacks and includes a border detection routine. The new texture library provides images for future work of researchers. Still a lot of work has to be done in the future to achieve an artificial vision system that is comparable to the human's visual capabilities. This is mainly due to the fact of missing computational resources. At least another decade of hardware development is needed to reach this goal. During this time more, better or even novel methods will be added to the Texture Classification System to improve its universal capabilities.
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Doan, Huong Thi Xuan. "Soft classification and land cover mapping from remotely sensed imagery". Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439513.

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Xia, Baiqiang. "Learning 3D geometric features for soft-biometrics recognition". Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10132/document.

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Abstract (sommario):
La reconnaissance des biomètries douces (genre, âge, etc.)trouve ses applications dans plusieurs domaines. Les approches proposéesse basent sur l’analyse de l’apparence (images 2D), très sensiblesaux changements de la pose et à l’illumination, et surtout pauvre en descriptionsmorphologiques. Dans cette thèse, nous proposons d’exploiterla forme 3D du visage. Basée sur une approche Riemannienne d’analysede formes 3D, nous introduisons quatre descriptions denses à savoir: lasymétrie bilatérale, la moyenneté, la configuration spatiale et les variationslocales de sa forme. Les évaluations faites sur la base FRGCv2 montrentque l’approche proposée est capable de reconnaître des biomètries douces.A notre connaissance, c’est la première étude menée sur l’estimation del’âge, et c’est aussi la première étude qui propose d’explorer les corrélationsentre les attributs faciaux, à partir de formes 3D
Soft-Biometric (gender, age, etc.) recognition has shown growingapplications in different domains. Previous 2D face based studies aresensitive to illumination and pose changes, and insufficient to representthe facial morphology. To overcome these problems, this thesis employsthe 3D face in Soft-Biometric recognition. Based on a Riemannian shapeanalysis of facial radial curves, four types of Dense Scalar Field (DSF) featuresare proposed, which represent the Averageness, the Symmetry, theglobal Spatiality and the local Gradient of 3D face. Experiments with RandomForest on the 3D FRGCv2 dataset demonstrate the effectiveness ofthe proposed features in Soft-Biometric recognition. Furtherly, we demonstratethe correlations of Soft-Biometrics are useful in the recognition. Tothe best of our knowledge, this is the first work which studies age estimation,and the correlations of Soft-Biometrics, using 3D face
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Siddiqui, Mujahuddin M., Shaikh M. Mobin, Irena Senkovska, Stefan Kaskel e Maravanji S. Balakrishna. "Novel zeotype frameworks with soft cyclodiphosphazane linkers and soft Cu₄X₄ clusters as nodes". Royal Society of Chemistry, 2014. https://tud.qucosa.de/id/qucosa%3A36036.

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Two novel cyclodiphosphazane cluster frameworks with Cu₄X₄ clusters as tetrahedral nodes and ferrocenyl cyclodiphosphazanes [Fe(η5-C5H₄)₂(PNtBu)₂] as ditopic linkers have been synthesized. These frameworks having sodalite topology display a unique integration of porosity and redox activity and offer new opportunities for the synthesis of zeotype frameworks with soft phosphorus-based ligands.
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Fröhner, Michael, e Manfred P. Wirth. "Etiologic Factors in Soft Tissue Sarcomas". Karger, 2001. https://tud.qucosa.de/id/qucosa%3A27622.

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Soft tissue sarcomas account for about 1% of all malignancies. The increase in incidence of soft tissue sarcomas during the recent decades may predominantly be attributed to AIDS-related Kaposi’s sarcoma; when this tumor is excluded, conclusive evidence for an age-adjusted increase is lacking. Beside the well investigated role of the human immunodeficiency virus 1 (HIV-1) and the human herpesvirus 8 (HHV-8) in the tumorigenesis of AIDS-related Kaposi’s sarcoma and several inherited disorders, considerable evidence support a relationship between occupational chemicals as vinyl chloride, phenoxyacetic acid herbicides, chlorphenols, dioxin, medicinal measures as Thorotrast exposure and therapeutic irradiation, and the development of soft tissue sarcoma. Hormones and chronic repair processes are further probably sarcoma-promoting factors. Considering the rarity of soft tissue sarcomas despite the vast portion that soft tissues comprise in the human body, additional knowledge on the tumorigenesis of soft tissue sarcomas might considerably contribute to the understanding of the etiologic pathways of malignant tumors in humans.
Weichteilsarkome stellen etwa 1% aller bösartigen Neubildungen. Der in den vergangenen Jahrzehnten beobachtete Inzidenzanstieg geht fast ausschließlich auf die rasante Zunahme an AIDS-assoziierten Kaposi-Sarkomen zurück. Bei Außerachtlassung dieses Tumors gibt es bisher keinen schlüssigen Beweis für eine wirkliche alterskorrigierte Häufigkeitszunahme der Weichteilsarkome. Neben der gut untersuchten Rolle des HIV-1-Virus und des humanen Herpes-Virus 8 bei der Entstehung des AIDS-assoziierten Kaposi-Sarkoms und einigen prädisponierenden genetischen Erkrankungen existieren starke Hinweise für einen Zusammenhang zwischen Industriegiften wie Vinylchlorid, Phenoxyessigsäure-Herbiziden, Chlorphenolen, Dioxinen, medizinischen Maßnahmen wie therapeutischer Bestrahlung oder dem Einsatz von Thorotrast, und der Entwicklung von Weichteilsarkomen. Hormone und chronische Reparaturprozesse sind weitere wahrscheinlich fördernde Einflüsse auf die Entstehung von Weichteilsarkomen. Die Tatsache, daß trotz des großen Anteils, den die Binde- und Stützgewebe an der Körpermasse stellen, nur selten maligne Tumoren von diesen Strukturen ausgehen, läßt hoffen, daß ein besseres Verständnis der an der Kanzerogenese von Weichteilsarkomen beteiligten Mechanismen in der Zukunft wichtige Erkenntnisse über die Entstehung menschlicher Tumoren liefern kann.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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Henke, E. F. Markus, Katherine E. Wilson e Iain A. Anderson. "Entirely soft dielectric elastomer robots". SPIE, 2017. https://tud.qucosa.de/id/qucosa%3A35126.

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Multifunctional Dielectric Elastomer (DE) devices are well established as actuators, sensors and energy harvesters. Since the invention of the Dielectric Elastomer Switch (DES), a piezoresistive electrode that can directly switch charge on and off, it has become possible to expand the wide functionality of DE structures even more. We show the application of fully soft DE subcomponents in biomimetic robotic structures. It is now possible to couple arrays of actuator/switch units together so that they switch charge between themselves on and off. One can then build DE devices that operate as self-controlled oscillators. With an oscillator one can produce a periodic signal that controls a soft DE robot { a DE device with its own DE nervous system. DESs were fabricated using a special electrode mixture, and imprinting technology at an exact pre-strain. We have demonstrated six orders of magnitude change in conductivity within the DES over 50% strain. The control signal can either be a mechanical deformation from another DE or an electrical input to a connected dielectric elastomer actuator (DEA). We have demonstrated a variety of fully soft multifunctional subcomponents that enable the design of autonomous soft robots without conventional electronics. The combination of digital logic structures for basic signal processing, data storage in dielectric elastomer ip-ops and digital and analogue clocks with adjustable frequencies, made of dielectric elastomer oscillators (DEOs), enables fully soft, self-controlled and electronics-free robotic structures. DE robotic structures to date include stiff frames to maintain necessary pre-strains enabling sufficient actuation of DEAs. Here we present a design and production technology for a first robotic structure consisting only of soft silicones and carbon black.
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Alorf, Abdulaziz Abdullah. "Primary/Soft Biometrics: Performance Evaluation and Novel Real-Time Classifiers". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96942.

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The relevance of faces in our daily lives is indisputable. We learn to recognize faces as newborns, and faces play a major role in interpersonal communication. The spectrum of computer vision research about face analysis includes, but is not limited to, face detection and facial attribute classification, which are the focus of this dissertation. The face is a primary biometric because by itself revels the subject's identity, while facial attributes (such as hair color and eye state) are soft biometrics because by themselves they do not reveal the subject's identity. In this dissertation, we proposed a real-time model for classifying 40 facial attributes, which preprocesses faces and then extracts 7 types of classical and deep features. These features were fused together to train 3 different classifiers. Our proposed model yielded 91.93% on the average accuracy outperforming 7 state-of-the-art models. We also developed a real-time model for classifying the states of human eyes and mouth (open/closed), and the presence/absence of eyeglasses in the wild. Our method begins by preprocessing a face by cropping the regions of interest (ROIs), and then describing them using RootSIFT features. These features were used to train a nonlinear support vector machine for each attribute. Our eye-state classifier achieved the top performance, while our mouth-state and glasses classifiers were tied as the top performers with deep learning classifiers. We also introduced a new facial attribute related to Middle Eastern headwear (called igal) along with its detector. Our proposed idea was to detect the igal using a linear multiscale SVM classifier with a HOG descriptor. Thereafter, false positives were discarded using dense SIFT filtering, bag-of-visual-words decomposition, and nonlinear SVM classification. Due to the similarity in real-life applications, we compared the igal detector with state-of-the-art face detectors, where the igal detector significantly outperformed the face detectors with the lowest false positives. We also fused the igal detector with a face detector to improve the detection performance. Face detection is the first process in any facial attribute classification pipeline. As a result, we reported a novel study that evaluates the robustness of current face detectors based on: (1) diffraction blur, (2) image scale, and (3) the IoU classification threshold. This study would enable users to pick the robust face detector for their intended applications.
Doctor of Philosophy
The relevance of faces in our daily lives is indisputable. We learn to recognize faces as newborns, and faces play a major role in interpersonal communication. Faces probably represent the most accurate biometric trait in our daily interactions. Thereby, it is not singular that so much effort from computer vision researchers have been invested in the analysis of faces. The automatic detection and analysis of faces within images has therefore received much attention in recent years. The spectrum of computer vision research about face analysis includes, but is not limited to, face detection and facial attribute classification, which are the focus of this dissertation. The face is a primary biometric because by itself revels the subject's identity, while facial attributes (such as hair color and eye state) are soft biometrics because by themselves they do not reveal the subject's identity. Soft biometrics have many uses in the field of biometrics such as (1) they can be utilized in a fusion framework to strengthen the performance of a primary biometric system. For example, fusing a face with voice accent information can boost the performance of the face recognition. (2) They also can be used to create qualitative descriptions about a person, such as being an "old bald male wearing a necktie and eyeglasses." Face detection and facial attribute classification are not easy problems because of many factors, such as image orientation, pose variation, clutter, facial expressions, occlusion, and illumination, among others. In this dissertation, we introduced novel techniques to classify more than 40 facial attributes in real-time. Our techniques followed the general facial attribute classification pipeline, which begins by detecting a face and ends by classifying facial attributes. We also introduced a new facial attribute related to Middle Eastern headwear along with its detector. The new facial attribute were fused with a face detector to improve the detection performance. In addition, we proposed a new method to evaluate the robustness of face detection, which is the first process in the facial attribute classification pipeline. Detecting the states of human facial attributes in real time is highly desired by many applications. For example, the real-time detection of a driver's eye state (open/closed) can prevent severe accidents. These systems are usually called driver drowsiness detection systems. For classifying 40 facial attributes, we proposed a real-time model that preprocesses faces by localizing facial landmarks to normalize faces, and then crop them based on the intended attribute. The face was cropped only if the intended attribute is inside the face region. After that, 7 types of classical and deep features were extracted from the preprocessed faces. Lastly, these 7 types of feature sets were fused together to train three different classifiers. Our proposed model yielded 91.93% on the average accuracy outperforming 7 state-of-the-art models. It also achieved state-of-the-art performance in classifying 14 out of 40 attributes. We also developed a real-time model that classifies the states of three human facial attributes: (1) eyes (open/closed), (2) mouth (open/closed), and (3) eyeglasses (present/absent). Our proposed method consisted of six main steps: (1) In the beginning, we detected the human face. (2) Then we extracted the facial landmarks. (3) Thereafter, we normalized the face, based on the eye location, to the full frontal view. (4) We then extracted the regions of interest (i.e., the regions of the mouth, left eye, right eye, and eyeglasses). (5) We extracted low-level features from each region and then described them. (6) Finally, we learned a binary classifier for each attribute to classify it using the extracted features. Our developed model achieved 30 FPS with a CPU-only implementation, and our eye-state classifier achieved the top performance, while our mouth-state and glasses classifiers were tied as the top performers with deep learning classifiers. We also introduced a new facial attribute related to Middle Eastern headwear along with its detector. After that, we fused it with a face detector to improve the detection performance. The traditional Middle Eastern headwear that men usually wear consists of two parts: (1) the shemagh or keffiyeh, which is a scarf that covers the head and usually has checkered and pure white patterns, and (2) the igal, which is a band or cord worn on top of the shemagh to hold it in place. The shemagh causes many unwanted effects on the face; for example, it usually occludes some parts of the face and adds dark shadows, especially near the eyes. These effects substantially degrade the performance of face detection. To improve the detection of people who wear the traditional Middle Eastern headwear, we developed a model that can be used as a head detector or combined with current face detectors to improve their performance. Our igal detector consists of two main steps: (1) learning a binary classifier to detect the igal and (2) refining the classier by removing false positives. Due to the similarity in real-life applications, we compared the igal detector with state-of-the-art face detectors, where the igal detector significantly outperformed the face detectors with the lowest false positives. We also fused the igal detector with a face detector to improve the detection performance. Face detection is the first process in any facial attribute classification pipeline. As a result, we reported a novel study that evaluates the robustness of current face detectors based on: (1) diffraction blur, (2) image scale, and (3) the IoU classification threshold. This study would enable users to pick the robust face detector for their intended applications. Biometric systems that use face detection suffer from huge performance fluctuation. For example, users of biometric surveillance systems that utilize face detection sometimes notice that state-of-the-art face detectors do not show good performance compared with outdated detectors. Although state-of-the-art face detectors are designed to work in the wild (i.e., no need to retrain, revalidate, and retest), they still heavily depend on the datasets they originally trained on. This condition in turn leads to variation in the detectors' performance when they are applied on a different dataset or environment. To overcome this problem, we developed a novel optics-based blur simulator that automatically introduces the diffraction blur at different image scales/magnifications. Then we evaluated different face detectors on the output images using different IoU thresholds. Users, in the beginning, choose their own values for these three settings and then run our model to produce the efficient face detector under the selected settings. That means our proposed model would enable users of biometric systems to pick the efficient face detector based on their system setup. Our results showed that sometimes outdated face detectors outperform state-of-the-art ones under certain settings and vice versa.
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Vasenkov, Sergey. "Structure-Transport relationship in organized soft matter systems by diffusion NMR". Diffusion fundamentals 16 (2011) 22, S. 1-2, 2011. https://ul.qucosa.de/id/qucosa%3A13754.

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Hazelbaker, Eric, Aakanksha Katihar, Monica Sanders, Amrish Menjoge e Sergey Vasenkov. "Structure-Transport relationship in organized soft matter systems by diffusion NMR". Diffusion fundamentals 16 (2011) 82, S. 1-10, 2011. https://ul.qucosa.de/id/qucosa%3A13827.

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In this paper we demonstrate and discuss the potentials of pulsed field gradient nuclear magnetic resonance (PFG NMR) at high magnetic field and high magnetic field gradients for uncovering the relationship between the structural and transport properties of soft matter systems. The reported diffusion studies are focused on room temperature ionic liquids and their mixtures with carbon dioxide or water as well as on multicomponent lipid bilayers. Both types of systems exhibit a well-defined structural organization on various length scales. Our experimental approach allows correlating this structural organization with the transport properties. The diffusion data were obtained by proton and carbon-13 PFG NMR. The experimental studies were in some cases complemented by dynamic Monte Carlo simulations.
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Libri sul tema "Soft classification"

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Ray, Kumar S. Soft Computing Approach to Pattern Classification and Object Recognition. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5348-2.

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H, Sobin L., e Enzinger Franz M, a cura di. Histological typing of soft tissue tumours. 2a ed. Berlin: Springer-Verlag, 1994.

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Rutkowski, Leszek. New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Rutkowski, Leszek. New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-40046-2.

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service), SpringerLink (Online, a cura di. Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept. New York, NY: Springer New York, 2012.

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World Health Organization (WHO). Pathology And Genetics of Tumours of the Soft Tissues And Bones: World Health Organization Classification of Tumours. LYON, FRANCE: The International Agency for Research on Cancer, 2003.

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Ben-Dov, Yair. A systematic catalogue of the soft scale insects of the world (Homoptera:Coccoidea:Coccidae): With data on geographical distribution, host plants, biology, and economics importance. Gainesville, Fla: Sandhill Crane Press, 1993.

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Buchbinder, Rachelle. The classification of soft tissue disorders of the neck and upper limb for epidemiological research. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Carthy, Joseph Noel. A database package to sort, search and statistically analyse and plot landuse/classification data. [s.l: The Author], 1988.

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Descartes, René. Discours de la méthode: Pour bien conduire sa raison, & chercher la vérité dans les sciences ; Plus, La Dioptrique ; Les Météores ; et, La Géométrie ; qui sont des essais de cette méthode. Lecce: Università degli Studi di Lecce, Dipartimento di Filosofia, 1987.

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Capitoli di libri sul tema "Soft classification"

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Kołakowska, Agata, e Witold Malina. "Sequential Classification". In Neural Networks and Soft Computing, 430–35. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_65.

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Rotaru, Florin, Silviu-Ioan Bejinariu, Cristina Diana Niţă, Ramona Luca, Mihaela Luca e Adrian Ciobanu. "Retinal Vessel Classification Technique". In Soft Computing Applications, 498–514. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62524-9_37.

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Zhang, Yingze, e Xin Xing. "Classifications of Soft-Tissue Injuries". In Clinical Classification in Orthopaedics Trauma, 635–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6044-1_13.

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Lee, Won Ki, e Dae Yul Yang. "Classification of Soft Tissue Filler". In Penile Augmentation, 71–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-46753-4_10.

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Balachander, Thiagarajan, e Ravi Kothari. "Localized Soft Subspace Pattern Classification". In International Conference on Advances in Pattern Recognition, 365–74. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0833-7_37.

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6

Nowicki, Robert, e Leszek Rutkowski. "Soft Techniques for Bayesian Classification". In Neural Networks and Soft Computing, 537–44. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_82.

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7

Hermanek, P., e L. H. Sobin. "Tumours of Bone and Soft Tissues". In TNM Classification of Malignant Tumours, 75–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82982-6_5.

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Várkonyi-Kóczy, A. R., B. Tusor e J. Bukor. "Data Classification Based on Fuzzy-RBF Networks". In Soft Computing Applications, 829–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18416-6_65.

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9

Fejjari, Asma, Karim Saheb Ettabaa e Ouajdi Korbaa. "Feature Extraction Techniques for Hyperspectral Images Classification". In Soft Computing Applications, 174–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52190-5_12.

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Ernst, H., W. W. Carlton, C. Courtney, M. Rinke, P. Greaves, K. R. Isaacs, G. Krinke, Y. Konishi, G. M. Mesfin e G. Sandusky. "Soft Tissue and Skeletal Muscle". In International Classification of Rodent Tumors. The Mouse, 361–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-07973-7_11.

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Atti di convegni sul tema "Soft classification"

1

Ionita, Andrei-Lucian, e Liviu Ciortuz. "MiRNA features for automated classification". In 2010 4th International Workshop on Soft Computing Applications (SOFA). IEEE, 2010. http://dx.doi.org/10.1109/sofa.2010.5565611.

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2

Buzera, M., e G. Prostean. "New algorithms used in the phases of automatic classification of products". In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254848.

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3

Balachander, T., e R. Kothari. "Oriented soft localized subspace classification". In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.759877.

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Bhatt, Malay, Rituraj Jain e C. K. Bhensdadia. "Project Classification Using Soft Computing". In 2009 International Conference on Advances in Computing, Control, & Telecommunication Technologies (ACT 2009). IEEE, 2009. http://dx.doi.org/10.1109/act.2009.137.

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Malagon, C., J. A. Barrio e D. Nieto. "Automatic image classification from Cherenkov telescopes using Bayesian ensemble of neural networks". In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254880.

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Ivaturi, Anjana, Ankita Singh, B. Gunanvitha e K. S. Chethan. "Soft Classification Techniques for Breast Cancer Detection and Classification". In 2020 International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2020. http://dx.doi.org/10.1109/iciem48762.2020.9160219.

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Lee, Taehyung, Jinil Kim, Jin Wook Kim, Sung-Ryul Kim e Kunsoo Park. "Detecting soft errors by redirection classification". In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526886.

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Gragnaniello, Diego, Giovanni Poggi, Giuseppe Scarpa e Luisa Verdoliva. "SAR despeckling based on soft classification". In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326287.

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Subramanya, Amarnag, e Jeff Bilmes. "Soft-supervised learning for text classification". In the Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1613715.1613857.

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Paderno, Pavel I., Evgeny A. Burkov, Elena A. Tolkacheva, Evgeny A. Lavrov e Olga E. Siryk. "Expert Classification: Probabilistic Estimates". In 2021 XXIV International Conference on Soft Computing and Measurements (SCM). IEEE, 2021. http://dx.doi.org/10.1109/scm52931.2021.9507116.

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Rapporti di organizzazioni sul tema "Soft classification"

1

Das, B. Evaluation of the point load strength for soft rock classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/304811.

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