Concept Formation As Induction

Total Page：16

File Type：pdf, Size：1020Kb

Concept formation as induction 0 + 0 + 0 + 0 + 0 + 0 + 0 + + 0 0 0 0 + 0 + Fit the data Overfit the data? Oververfitting and underfitting in induction • Rome has a subway -> Rome has a subway • Rome, Venice have subways -> -> All Italian cities have subways • Rome, Venice, Paris have subways -> All cities beginning with R, V or P have subways [ a natural kind?] -> All European cities have subways [a compositional concept] • Rome, Venice, Paris, New York have subways -> All cities have subways • But Saint-Jeannet, France does not have a subway -> All large European cities have subways • …etc. Natural kinds • But can you induce “large European cities” as a concept with stable properties? • This is a complex compositional concept - Does its properties follow from its constituent elements? • Is it a natural kind? - i.e. a stable phenomenon of the world as it exists, with consistent properties • We seek to “carve nature at its joints” Puzzles of induction • Hume’s problem The basic problem of induction • Hempel’s paradox All ravens are black (∀x raven(x) → black(x)), is logically equivalent to All non-black things are non-ravens (∀x ~black(x) → ~raven(x)) - So a white swan confirms the generalization as much as a black raven • Goodman’s problem (“grue/bleen”) Grue and Bleen (Goodman, 1955) Green < time t Blue < time t Grue = = Bleen { Blue ≥ time t Green ≥ time t} Grue < time t Bleen < time t Green = = Blue { Bleen ≥ time t Grue ≥ time t } Grue and bleen are no more complex than green and blue The Ugly Duckling theorem (Watanabe) • A feature is a subset of objects • But all objects in a set have the same Harry Ginny number of features in common with other sets! Ron Hermione Male vs Female Married couples Weasleys vs not • Therefore all objects are equally similar to all other objects (share the same number of features). →You have to place constraints on what counts as a “feature,” or categories are meaningless.

Copy Link

Recommended publications

Blumer's Symbolic Interactionism: Methodological Implications
University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 1-1-1971 Blumer's symbolic interactionism: Methodological implications. Jan Spurway Marks University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Marks, Jan Spurway, "Blumer's symbolic interactionism: Methodological implications." (1971). Electronic Theses and Dissertations. 6691. https://scholar.uwindsor.ca/etd/6691 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email ([email protected]) or by telephone at 519-253-3000ext. 3208. - BLUMER'S SYMBOLIC INTERACTIONISM: METHODOLOGICAL IMPLICATIONS A THESIS 'SUBMITTED TO THE FACULTY OF GRADUATE STUDIES THROUGH THE DEPARTMENT OF SOCIOLOGY AND ANTHROPOLOGY IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS AT THE UNIVERSITY OF WINDSOR JAN SPURWAY MARKS UNIVERSITY OF WINDSOR 1971 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: EC53094 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted.
[Show full text]
The Relevance of Carnap's Inductive Logic for Supervised Classification
University Utrecht Artificial Intelligence Master’s Thesis The Relevance of Carnap’s Inductive Logic for Supervised Classification Supervisor: Author: Dr. Janneke van Lith Wessel de Jong Dr. Sander Beckers March 19, 2019 Department of Theoretical Philosophy University Utrecht Abstract Recent advances in the field of machine learning have cast new light on the long-standing debate within the philosophy of science between falsificationists and inductivists. As a result of many failed attempts at justifying inductive in- ference, falsificationism has tended to be preferred over inductivism. However, Gillies claims that the success of certain machine learning procedures neces- sarily leads to the conclusion that inductivism has shown to be successful [19]. In this thesis I will challenge this claim by examining how criticism voiced against the inductive logic of one of the central figures of the inductivism, Rudolf Carnap, might still be relevant for modern day machine learning pro- cedures. Since a general claim about all machine learning procedures would be prone to counterexamples this thesis will be limited to only linear models for supervised classification. I will consider how three well-known critiques that strongly undermined Carnap’s inductive methods might still apply to such classification models. Department of Theoretical Philosophy University Utrecht Contents 1 Introduction3 2 Machine Learning7 2.1 Supervised Classification.........................7 2.2 Data Representation...........................9 2.3 Training.................................. 11 2.4 Testing................................... 15 3 Carnap’s Inductive Logic 18 3.1 Early Carnap............................... 19 3.2 Late Carnap................................ 22 3.2.1 Confirmation Functions..................... 23 3.2.2 Attribute Spaces......................... 24 3.2.3 Similarity Influence.......................
[Show full text]
Data-Driven Approach to Information Sharing Using Data Fusion and Machine Learning
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by HSN Open Archive Data-driven Approach to Information Sharing using Data Fusion and Machine Learning Lars Christian Andersen 06-01-2016 Master’s Thesis Master of Science in Information Security 30 ECTS Department of Computer Science and Media Technology Norwegian University of Science and Technology, 2016 Supervisor 1: Prof. Katrin Franke Supervisor 2: Andrii Shalaginov Data-driven Approach to Information Sharing using Data Fusion and Machine Learning Lars Christian Andersen 06-01-2016 Data-driven Approach to Information Sharing using Data Fusion and Machine Learning Abstract The number of security incidents worldwide is increasing, and the capabilities to detect and react is of uttermost importance. Intrusion Detection Systems (IDSs) are employed in various locations in networks to identify malicious activity. These sensors produce large amounts of data, which are fused and reduced. It is necessary to determine how to perform such fusion and reduction of data from heterogeneous sources. IDS is known to produce a high amount of false positives which create a high workload for human analysts at Security Operation Center (SOC). To ensure scalability, systems for reducing and streamlining the detection process is critical. The application of Threat Intelligence (TI) in information se- curity for detection and prevention is widespread. When performing sharing of TI, it must be ensured that the data is reliable and trustworthy. Further, it must be guaranteed that the sharing process leaks sensitive data. This thesis has proposed a process model describing the process of fusion and reduction of heterogeneous sensor data and TI in intrusion detection.
[Show full text]
No Free Lunch Theorem, Ugly Duckling Theorem
Algorithm Independent Topics Lecture 6 Jason Corso SUNY at Buffalo Feb. 23 2009 J. Corso (SUNY at Buffalo) Algorithm Independent Topics Lecture 6 Feb. 23 2009 1 / 45 Introduction Now that we’ve built an intuition for some pattern classifiers, let’s take a step back and look at some of the more foundational underpinnings. Algorithm-Independent means those mathematical foundations that do not depend upon the particular classifier or learning algorithm used; techniques that can be used in conjunction with different learning algorithms, or provide guidance in their use. Specifically, we will cover 1 Lack of inherent superiority of any one particular classifier; 2 Some systematic ways for selecting a particular method over another for a given scenario; 3 Methods for integrating component classifiers, including bagging, and (in depth) boosting. J. Corso (SUNY at Buffalo) Algorithm Independent Topics Lecture 6 Feb. 23 2009 2 / 45 Simply put, No! If we find one particular algorithm outperforming another in a particular situation, it is a consequence of its fit to the particular pattern recognition problem, not the general superiority of the algorithm. NFLT should remind you that we need to focus on the particular problem at hand, the assumptions, the priors, the data and the cost. Recall the fish problem from earlier in the semester. For more information, refer to http://www.no-free-lunch.org/. Lack of Inherent Superiority No Free Lunch Theorem No Free Lunch Theorem The key question we inspect: If we are interested solely in the generalization performance, are there any reasons to prefer one classifier or learning algorithm over another? J.
[Show full text]
CS251 Spring 2014 Lecture 18
CS251 Spring 2014 Lecture 18 Stephanie R Taylor March 17, 2014 1 Fundamentals of Data Mining and Machine Learning The goal for the course is to learn how to find meaningful patterns in data. We do that through judicious use of pre-processing, visualization and analysis. We have already seen one method { PCA. For the rest of the semester, we will be looking at more methods of analysis. Before we go further, let's get some context. There are two, overlapping, subfields of computer science in which people are trying to find patterns in data. They are machine learning and data mining. Machine learning. Machine learning, a branch of artificial intelligence, is about the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders. (grabbed from the Wikipedia page on machine learning March 29, 2013) Data Mining. The goal of data mining is finding and describing structural patterns in data (from the textbook). A rule of thumb for the difference between machine learning and data mining is that machine learning focuses on prediction, based on known properties learned from the training data. Data mining focuses on the discovery of (previously) unknown properties on the data. (grabbed from the Wikipedia page on machine learning March 28, 2013). We will be studying four categories of algorithms in this course. 1. Dimensionality reduction: Principal component analysis allows us to concisely capture struc- ture in the data by forming a new (smaller) set of features.
[Show full text]
The Metaphysical Significance of the Ugly Duckling Theorem
The Metaphysical Significance of the Ugly Duckling Theorem Abstract According to Satosi Watanabe's \theorem of the ugly duckling", the number of (possible) predicates satisfied by any two different particulars is a constant, which does not depend on the choice of the two particulars. If the number of (possible) predicates satisfied by two particulars is their number of properties in common, and the degree of resemblance between two particulars is a function of their number of properties in common, then it follows that the degree of resemblance between any two different particulars is a constant, which does not depend on the choice of the two particulars either. Avoiding this absurd conclusion requires questioning assumptions involving infinity in the proof or interpretation of the theorem, adopting a sparse conception of properties according to which not every (possible) predicate corresponds to a property, or denying that degree of resemblance is a function of number of properties in common. After arguing against the first two options, this paper argues for a version of the third which analyses degrees of resemblance in terms of degrees of naturalness of common and uncommon properties. In the course of doing so, it presents a novel account of degrees of naturalness. 1 Introduction \...the point of philosophy," according to Bertrand Russell, \is to start with something so simple as not to seem worth stating, and to end with something so paradoxical that no one will believe it" (Russell, 1918, 514). The argument discussed in this paper, which proceeds from platitudinous or nearly provable premises about predicates and properties to the barely believable conclusion that any two different particulars resemble each other to the same degree, does not fall far short of Russell's goal.
[Show full text]
THESIS (6.743Mb)
University of Plymouth PEARL https://pearl.plymouth.ac.uk 04 University of Plymouth Research Theses 01 Research Theses Main Collection 1992 CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE SIMPSON, ROBERT GILMOUR http://hdl.handle.net/10026.1/1843 University of Plymouth All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE ROBERT GILMOUR SIMPSON, B.A., M.Sc A thesis submitted in partial fulfilment of the requirements of the University of Plymouth for the degree of Doctor of Philosophy October 1992 University of Plymouthin collaboration with the Plymouth Marine Laboratory REFERENCE ONLY 90 0166561 3 LIBRARY STORE UNIVERSITY OF PLYMOUTH ir UBRARY SERVICES 006 .3^ S-XM Contl To all my family, for their encouragement, ROBERT GILMOUR SIMPSON Abstract. Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C.
[Show full text]
Journal of the American Philosophical Association
Journal of the American Philosophical Association http://journals.cambridge.org/APA Additional services for Journal of the American Philosophical Association: Email alerts: Click here Subscriptions: Click here Commercial reprints: Click here Terms of use : Click here Phenomenal Intentionality and the Problem of Representation WALTER OTT Journal of the American Philosophical Association / Volume 2 / Issue 01 / March 2016, pp 131 - 145 DOI: 10.1017/apa.2016.4, Published online: 30 March 2016 Link to this article: http://journals.cambridge.org/abstract_S205344771600004X How to cite this article: WALTER OTT (2016). Phenomenal Intentionality and the Problem of Representation. Journal of the American Philosophical Association, 2, pp 131-145 doi:10.1017/apa.2016.4 Request Permissions : Click here Downloaded from http://journals.cambridge.org/APA, IP address: 71.207.141.202 on 31 Mar 2016 Journal of the American Philosophical Association (2016) 131–145 C American Philosophical Association doi: 10.1017/apa.2016.4 Phenomenal Intentionality and the Problem of Representation abstract: According to the phenomenal intentionality research program, a state’s intentional content is ﬁxed by its phenomenal character. Defenders of this view have little to say about just how this grounding is accomplished. I argue that without a robust account of representation, the research program promises too little. Unfortunately, most of the well-developed accounts of representation— asymmetric dependence, teleosemantics, and the like—ground representation in external relations such as causation. Such accounts are inconsistent with the core of the phenomenal intentionality program. I argue that, however counterintuitive it may seem, the best prospect for explaining how phenomenal character represents is an appeal to resemblance.
[Show full text]
IP Algorithm Applied to Proteomics Data
Brigham Young University BYU ScholarsArchive Theses and Dissertations 2004-11-30 IP Algorithm Applied to Proteomics Data Christopher Lee Green Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Statistics and Probability Commons BYU ScholarsArchive Citation Green, Christopher Lee, "IP Algorithm Applied to Proteomics Data" (2004). Theses and Dissertations. 202. https://scholarsarchive.byu.edu/etd/202 This Selected Project is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. IP Algorithm Applied to Proteomics Data By Christopher L. Green A project submitted to the faculty of Brigham Young University In partial fulfillment of the requirements for the degree of Master of Science Department of Statistics Brigham Young University December 2004 BRIGHAM YOUNG UNIVERSITY GRADUATE COMMITTEE APPROVAL of a project submitted by Christopher L. Green This project has been read by each member of the following graduate committee and by majority vote has been found to be satisfactory. _____________________ ________________________ Date H. Dennis Tolley Chair _____________________ ________________________ Date Gilbert W. Fellingham _____________________ ________________________ Date Scott D. Grimshaw BRIGHAM YOUNG UNIVERSITY As chair of the candidate’s graduate committee, I have read the project of Christopher Green in its final form and have found that (1) its format, citations, and bibliographical style are consistent and acceptable and fulfill university and department style requirements; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the graduate committee and is ready for submission to the university library.
[Show full text]
Lezione 3 - Clustering and Classification Introduction
Mining di Dati Web Lezione 3 - Clustering and Classification Introduction • Clustering and classification are both learning techniques • They learn functions describing data • Clustering is also known as Unsupervised Learning • Classification is known as Supervised Learning Clustering • From Wikipedia, Data clustering: Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure Clustering A Toy Example Sneak a Peak at Clustering: K-Means • Randomly generate k clusters and determine the cluster centers or directly generate k seed points as cluster centers • Assign each point to the nearest cluster center. • Recompute the new cluster centers. • Repeat until some convergence criterion is met (usually that the assignment hasn't changed). d_intra or d_inter? That is the question! • d_intra is the distance among elements (points or objects, or whatever) of the same cluster. • d_inter is the distance among clusters. • Questions: • Should we use distance or similarity? • Should we care about inter cluster distance? • Should we care about cluster shape? • Should we care about clustering? d_intra, d_inter inter d dintra Distance Functions • Informal definition: • The distance between two points is the length of a straight line segment between them. • A more formal definition: • A distance between two points P and Q in a metric space is d(P,Q), where d is the distance function that defines the given metric space.
[Show full text]
Feature Extraction and Static Analysis for Large-Scale Detection of Malware Types and Families
Feature Extraction and Static Analysis for Large-Scale Detection of Malware Types and Families Lars Strande Grini Master’s Thesis Master of Science in Information Security 30 ECTS Department of Computer Science and Media Technology Gjøvik University College, 2015 Avdeling for informatikk og medieteknikk Høgskolen i Gjøvik Postboks 191 2802 Gjøvik Department of Computer Science and Media Technology Gjøvik University College Box 191 N-2802 Gjøvik Norway Feature Extraction and Static Analysis for Large-Scale Detection of Malware Types and Families Lars Strande Grini 15/12/2015 Feature Extraction and Static Analysis for Large-Scale Detection of Malware Types and Families Abstract There exist different methods of identifying malware, and widespread method is the one found in almost every antivirus solution on the market today; the signature based ap- proach. This approach uses a one-way cryptographic function to generate a unique hash of each ﬁle. Afterwards, each hash is checked against a database of hashes of known mal- ware. This method provides close to none false positives, but this does also mean that this approach can only detect previously known malware, and will in many cases also provide a number of false negatives. Malware authors exploit this weakness in the way that they change a small part of the malicious code, and thereby changes the entire hash of the ﬁle, which then leaves the malicious code undetectable until the sample is discovered, analyzed and updated in the vendors database(s). In the light of this relatively easy mit- igation for malware authors, it is clear that we need other ways to identify malware.
[Show full text]
The Metaphysics of Similarity and Analogical Reasoning
THE METAPHYSICS OF SIMILARITY AND ANALOGICAL REASONING A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN PHILOSOPHY by Jarrod Brown August 2018 Dissertation Committee Members: Tamara Albertini Aridam Chakrabarti (chairperson) Vrinda Dalmiya George Tsai Amy Schafer Jesse Knutson Keywords: analogy, similarity, comparative philosophy, relations 1 ABSTRACT This work introduces the importance of similarity and analogy to philosophy, argues that analogy should be seen as “similarity based reasoning,” overviews different philosophical discussions to illustrate the scope of similarity-based reasoning, and introduces the assumptions for similarity-based reasoning that form the central topics of the present work. It demonstrates that approaches that reduce or identify relations to non-relational ontological categories fail primarily through the strategy of seeking truthmakers for relational claims. It takes up the related problems of co-mannered relations, substitution instances, individuating relations and similarities. It attacks the notion that substitution instances provide a non-relational account of relations and provides a proof that similarity is a more fundamental concept than substitution. Given, however, that similarity is relational, it argues relations are non-reducible to non-relational entities, but that if given the notion of relating in general and of similarity, one can construct a non-reductive theory of relations that can individuate all relations, including similarity relations themselves. This provides a workable theory of relations, but does not solve problems related to the epistemology of relations and similarity. Sanskritic debates concerning the metaphysical nature of similarity and its knowability are explored through contrasting the views of four Indian schools of thought: the Buddhist, Nyāya-Vaiśeṣika, Bhaṭṭa Mīmāṃsāka, and Prābhākara Mīmāṃsā.
[Show full text]