Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), Big Data analytics, machine learning, training schemes, crowd-sourcing (experts), feature space, clustering, classification, SVM, similarity measures, modified Boltzmann Machines, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability, Z-number, Z-Web, Z-factor, rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, data compression, event-centric social network, Image Ad Network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for feature detection with Z-factors, said system comprising: one or more layers of stochastic units; one or more weighted links, associating a first stochastic unit of said one or more layers of stochastic units with one or more linked units; a layer of input units, connected to an input device; wherein a set of data is input to one or more of clamped input units in said layer of input units; a set of detected features, stored on a first memory unit; wherein said set of detected features are associated with a top layer of said one or more layers of stochastic units; wherein an energy measure corresponding to said set of data is determined based on factors comparing said one or more of clamped input units, said one or more weighted links, and said one or more layers of stochastic units; wherein a Z-factor corresponding to said set of detected features is determined based on said energy measure and a baseline. 2. The system for feature detection with Z-factors as recited in claim 1, wherein said system is feeding an ad network. 3. The system for feature detection with Z-factors as recited in claim 1, wherein said system is fed by an ad network. 4. The system for feature detection with Z-factors as recited in claim 1, wherein said system is part of an image ad network. 5. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes objects. 6. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes people. 7. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes emotions. 8. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes human behavior. 9. The system for feature detection with Z-factors as recited in claim 1, wherein said system matches ads with merchant catalogs. 10. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes natural language. 11. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes voice. 12. The system for feature detection with Z-factors as recited in claim 1, wherein said system is a part of a search engine. 13. The system for feature detection with Z-factors as recited in claim 1, wherein said system is connected to or associated with a Z-web. 14. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes patterns in social networks. 15. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes actions in video. 16. The system for feature detection with Z-factors as recited in claim 1, wherein said system is associated with a classifier. 17. The system for feature detection with Z-factors as recited in claim 1, wherein said system recognizes components of an object. 18. The system for feature detection with Z-factors as recited in claim 1, wherein said system learns by itself. 19. A system for feature detection with reliability, said system comprising: one or more layers of stochastic units; one or more weighted links, associating a first stochastic unit of said one or more layers of stochastic units with one or more linked units; a layer of input units, connected to an input device; wherein a set of data is input to one or more of clamped input units in said layer of input units; a set of detected features, stored on a first memory unit; wherein said set of detected features are associated with a top layer of said one or more layers of stochastic units; wherein an energy measure corresponding to said set of data is determined based on factors comparing said one or more of clamped input units, said one or more weighted links, and said one or more layers of stochastic units; wherein a reliability measure corresponding to said set of detected features is determined based on said energy measure and a baseline. 20. A system for feature detection with conformity measure, said system comprising: one or more layers of stochastic units; one or more weighted links, associating a first stochastic unit of said one or more layers of stochastic units with one or more linked units; a layer of input units, connected to an input device; wherein a set of data is input to one or more of clamped input units in said layer of input units; a set of detected features, stored on a first memory unit; wherein said set of detected features are associated with a top layer of said one or more layers of stochastic units; wherein an energy measure corresponding to said set of data is determined based on factors comparing said one or more of clamped input units, said one or more weighted links, and said one or more layers of stochastic units; wherein a conformity measure corresponding to said set of detected features is determined based on said energy measure and a baseline.