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GDB
Dear Students,

GDB for CS607 subject will open for two days from Thursday 30th January 2020 to Friday 31th January 2020. (48 Hours only). You will be required to post your comments on following GDB Topic.

GDB:

In machine-learning problem space can be represented through concept space, instance space version space and hypothesis space. These problem spaces used the conjunctive space and is very restrictive one and also in the above-mentioned representations of problem spaces, it is not sure that the true concept lies within conjunctive space.

GDB Topic:

Discuss the case if we have a bigger search space and want to overcome the restrictive nature of conjunctive space, then how can we represent our problem space. Secondly in a given scenario which algorithm is used for our problem space to represent the learning problem.

Note:- Keep your comments brief and to the point, GDB will not be accepted via email in any case.


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In that case, we can represent all the concepts in the concept space

is it right?

plz send GDB Solution

GDB CS607
The “learning” part of mechanism learning refers to an algorithm’s ability to find patterns in data to self-improve the machine’s outcomes, i.e. to use existing data to predict unknowns. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction.
A version space description consists of two complementary trees:
1. One that contains nodes connected to overly general models, and
2. One that contains nodes connected to overly specific models

CS607 GDB 2019-2020
The typicalness framework partially overcomes this problem but it depends heavily on the learning algorithm used. To overcome these problems of the presented frameworks we propose to consider version spaces [8, 9, 13] as an approach to reliable instance classification. The key idea is to construct version spaces containing hypotheses of the target concepts to be learned or their close approximations. …
Inductive Leap: A learner should be able to generalize training data using prior assumptions in order to classify unseen instances. The generalization is known as inductive leap and our prior assumptions are the inductive bias of the learner. Inductive Bias (prior assumptions) of Candidate-Elimination Algorithm is that the target concept can be represented by a conjunction of attribute values, the target concept is contained in the hypothesis space and training examples are correct.… instance misclassifications are blocked. To demonstrate version spaces for reliable classification we implement the unanimous-voting rule by testing version spaces for collapse [4, 13]. We show that testing can be done by any learning algorithm and use support vector machines (SVM) [3, 16]. …… Version spaces are sets of hypotheses consistent with training data [8, 9, 13]. Definition 1. …
Boundary sets are sets of minimal and maximal descriptions in version spaces. It was proven that they correctly represent version spaces [5, 11]. An analysis of boundary sets shows that their size can grow exponentially in the number of training instances [1]. …… An analysis of boundary sets shows that their size can grow exponentially in the number of training instances [1]. To overcome this problem alternative version-space representations were introduced in [2, 3, 4, 8, 9, 10, 11, 12, 15]. They extend the scope of concept languages for which version spaces are efficiently applicable. …… To solve this problem we propose a new version-space representation called adaptable boundary sets. Depending on the memory requirements the representation can be adjusted to a version-space representation between boundary sets and instance-based boundary sets [11, 12]. Thus, the choice of proper version-space representations can be realised dynamically during the learning phase.

CS607 GDB 2019-2020


The typicalness framework partially overcomes this problem but it depends heavily on the learning algorithm used. To overcome these problems of the presented frameworks we propose to consider version spaces [8, 9, 13] as an approach to reliable instance classification. The key idea is to construct version spaces containing hypotheses of the target concepts to be learned or their close approximations. …
Inductive Leap: A learner should be able to generalize training data using prior assumptions in order to classify unseen instances. The generalization is known as inductive leap and our prior assumptions are the inductive bias of the learner. Inductive Bias (prior assumptions) of Candidate-Elimination Algorithm is that the target concept can be represented by a conjunction of attribute values, the target concept is contained in the hypothesis space and training examples are correct.… instance misclassifications are blocked. To demonstrate version spaces for reliable classification we implement the unanimous-voting rule by testing version spaces for collapse [4, 13]. We show that testing can be done by any learning algorithm and use support vector machines (SVM) [3, 16]. …… Version spaces are sets of hypotheses consistent with training data [8, 9, 13]. Definition 1. …
Boundary sets are sets of minimal and maximal descriptions in version spaces. It was proven that they correctly represent version spaces [5, 11]. An analysis of boundary sets shows that their size can grow exponentially in the number of training instances [1]. …… An analysis of boundary sets shows that their size can grow exponentially in the number of training instances [1]. To overcome this problem alternative version-space representations were introduced in [2, 3, 4, 8, 9, 10, 11, 12, 15]. They extend the scope of concept languages for which version spaces are efficiently applicable. …… To solve this problem we propose a new version-space representation called adaptable boundary sets. Depending on the memory requirements the representation can be adjusted to a version-space representation between boundary sets and instance-based boundary sets [11, 12]. Thus, the choice of proper version-space representations can be realised dynamically during the learning phase.

As we know that the learning part of machine learning refers to an algorithm’s ability to find patterns in data to self-improve the machine’s outcomes. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences.
A version space algorithm is used for this cenario description of this algorithm consists of two complementary trees that contains nodes connected to overly general models, and one that contains nodes connected to overly specific models.

CS607 GDB 2019-2020

Decision trees is the algorithm that is capable of learning disjunctive expressions. It avoids the different of restricted hypothesis spaces and a practical method for inductive inference. In our case, we therefore need use the decision tee. We can apply decision trees to different problems where we have a bigger search space such as medical patients by their diseases,equipment malfunction by their cause,and the load applicant by their chances of defaulting on payments etc.

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